The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic ...The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.展开更多
Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefor...Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.展开更多
The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environmen...The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.展开更多
Today,environmental studies based on satellite imagery are known as making valuable contributions to the dynamics and spatial prediction of sensitive or complex ecosystems such as wide protected areas and represent su...Today,environmental studies based on satellite imagery are known as making valuable contributions to the dynamics and spatial prediction of sensitive or complex ecosystems such as wide protected areas and represent sustainable decision tools.The Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs,habitats for wildlife conservation lack substantial investigations on the vegetation dynamics.Despite the protection measures they benefit from,these reserves remain dependent on climatic hazards that can influence their stability.The present study is innovative since it applied remote sensing techniques combinedwith climate records fromthe last thirty years to analyze the past dynamics of land use and climate changes to predict the future trends of the vegetation cover of the two national parks in Benin,as well as their peripheries.The methodology used remote sensing and Geographic Information System(GIS)techniques that allowed the supervised classification of Landsat images from 1985,2000 and 2015.Climatic data were combined in R software to identify the break periods for climatic parameters.Finally,the predictive vegetation cover for the year 2030 was made by combining vegetation and climatic data in the“Land Change Modeler”extension.Results show ten land use and land cover classes which are the agglomerations,mosaics of fields and fallows,water bodies,dense forests,gallery forests,clear forests and wooded savannahs,swamp forests and shrubby wooded savannahs,saxicolous savannahs and bare ground.The natural vegetation decreased from 90.85%in 1985 to 83.54% in 2000 then to 79.56% in 2015,representing a decline of 11.39%over a study period of 30 years.The analysis of the climatic curves revealed the presence of a break,meaning drought frequencies.Thepredictivemodeling showed that land use units projected up to the year 2030 are consistent with past trends,but with the continued expansion of fields and fallows(2%)instead of the natural vegetation.This study not only provides good insights useful in the sustainable management of the Biosphere Reserves but will also motivate many other researches towards such ecosystems.展开更多
Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked popu...Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked populations,the lack of bank controls,and inflation.This study investigates the global patterns of cryptocurrency adoption using Generalized Linear Models and Spatial Autoregressive Models.This research introduces a novel perspective on global cryptocurrency adoption using spatial models.Our findings reveal that cryptocurrency adoption is significantly influenced by economic instability,infrastructure availability,and spatial dynamics,with higher adoption rates in countries with limited access to traditional financial systems.展开更多
Picture is a means of objects’representation and inter-subject communication,where various ways of spatial modelling interact.Unlike arbitrary signs,picture not only represents something different from itself,but sho...Picture is a means of objects’representation and inter-subject communication,where various ways of spatial modelling interact.Unlike arbitrary signs,picture not only represents something different from itself,but shows the represented objects for viewer’s perception.Therefore,the external modelling in form of a material bearer is connected always with internal modelling of depicted objects.Not only perceptual images,but also internal models of other psychical levels participate in creation and interpretation of the pictures.These are,on the one hand,images of the apperceptual and conceptual levels,where schemes of represented objects and their verbal interpretations are formed.On the other hand,the images of sensorial level participate also in the internal modelling.All these mental models interact differently by artists and viewers and influence organization of external pictorial models mediating their contacts.展开更多
The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination ...The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.展开更多
The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province,China,a region known for its rich biodiversity.This study aims to understand the trade-offs between tobacco farming ...The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province,China,a region known for its rich biodiversity.This study aims to understand the trade-offs between tobacco farming and higher plant species diversity,and to identify priority counties for conservation.We employed an integrated approach combining species distribution modeling,GIS overlay analysis,and empirical spatial regression to em pirically assess the impact of tobacco farming intensity on biodiversity risk.Our findings reveal a compelling negative spatial correlation between tobacco farming expansion and higher plant species diversity.Specifically,southern counties in Wenshan and Honghe prefectures are major priority areas of conservation that exhibit signif icant spatial correlations between biodiversity risks and high tobacco farming intensity.Quantitatively,at county level,a 1%increase in tobacco farming area corresponds to a 0.094%decrease in endemic higher plant species richness across the entire province.These results underscore the need for targeted and region-specific regulations to mitigate biodiversity loss and promote sustainable development in Yunnan Province.The integrated approach used in this study provides a comprehensive assessment of the tobacco-biodiversity trade-offs,offering actionable insights for policymaking.展开更多
In this paper,we consider the following spatial Solow-Swan model with density-dependent motion■whereσ>0,α∈(0,1)andΩ⊂ℝn(n≥1)is a bounded domain with smooth boundary andϕ∈C3([0,∞)),ϕ(s)>0 for all s≥0.We p...In this paper,we consider the following spatial Solow-Swan model with density-dependent motion■whereσ>0,α∈(0,1)andΩ⊂ℝn(n≥1)is a bounded domain with smooth boundary andϕ∈C3([0,∞)),ϕ(s)>0 for all s≥0.We prove that if■then there exists a unique time-globally classical solution(u,v)for all n≥1,such a solution is bounded and satisfies u≥0,v>0.Moreover,we show that the above solution will convergence to the steady state(1,1)exponentially in L^(∞)as t→∞.展开更多
Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still...Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.展开更多
To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm ...To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.展开更多
Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbani...Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.展开更多
This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the netwo...This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.展开更多
This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them...This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency.展开更多
Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP mo...Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP model, G\|GTP is used for the real\|3D modeling of subsurface geological bodies, and E\|GTP is used for the real\|3D modeling of subsurface engineering excavations.In the light of the discussions on the features and functions of E 2 GIS, the modeling principles of G\|GTP and E\|GTP are introduced. The two models couple together seamlessly to form an integral model for subsurface spatial objects including both geological bodies and excavations. An object\|oriented integral real\|3D data model and integral spatial topological relations are discussed.展开更多
The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively popu...The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively population data with other data including natural resources, environment, society and economy, build 1km GRIDs of natural resources reserves per person, population density and other economic and environmental data, which are necessary to the national management and macro adjustment and control of natural resources and dynamic monitoring of population. In order to establish population information system serving national decision making, three steps ought to be followed:1) establishing complete geographical spatial data foundation infrastructure including the establishment of electric map of residence with high resolution using topographical map with large scale and high resolution satellite remote sensing data, the determination of attribute information of housing and office buildings, and creating complete set of attribute database and rapid data updating; 2) establishing complete census systems including improving the transformation efficiency from census data to digital database and strengthening the link of census database and geographical spatial database, meanwhile, the government should attach great importance to the establishment and integration of population migration database; 3) considering there is no GIS software specially serving the analysis and management of population data, a practical approach is to add special modules to present software system, which works as a bridge actualizing the digitization and spatialization of population geography research.展开更多
Daihai Lake, a modern lacustrine rift basin, located in Inner Mongolia, North China, serves as an important modern analog for understanding deltaic depositional processes in an active rift setting. Two of the deltas ...Daihai Lake, a modern lacustrine rift basin, located in Inner Mongolia, North China, serves as an important modern analog for understanding deltaic depositional processes in an active rift setting. Two of the deltas （Yuanzigou delta and Bulianghe delta） on the margins of Daihai Lake were surveyed to compare and contrast stacking patterns using aerial photographs, field trenching and sediment sampling. Shallow cores and trench data collected from the margins of Daihai Lake indicate that a variety of depositional processes have been active since Daihai Lake formed. Two 3-D sedimentation models which employ chronostratigraphic correlation technique were generated. The chronostratigraphic sedimentation models predict and represent the architectures and sand-body continuity of sediments. Stratigraphical coincidence of the broad sheeted drifts and channel erosion suggests a coupling between downslope and alongslope processes. Distributary mouth bars are prevalent in the front of deltas on steeper slopes due to the dominance of down-slope flows. On the contrary, the along-slope currents favor the development of distal bar deposits with sheeted sandbodies on gentle depositional slopes. This study provides an insight into the architecture of complex sedimentary facies associated with highlighting key differences between downslope flows and alongslope currents. The distribution of sand within these deltas is of particular interests, with applications in understanding the architecture of hydrocarbon reservoirs formed in lacustrine rift basin.展开更多
Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are st...Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.展开更多
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199...This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.展开更多
Energy planning and solar plant site selections are vital strategic decisions and one of the most complex executive challenges in the interconnected procedures.It is essential to study the potential renewable energy s...Energy planning and solar plant site selections are vital strategic decisions and one of the most complex executive challenges in the interconnected procedures.It is essential to study the potential renewable energy sources in Afghanistan to select the most sustainable sites for solar power production in populated cities.This study is based on the combination of a Geographic Information System,Remote sensing,and multi-criteria decision-making technique to evaluate the optimal placement of photovoltaic solar power plants in the Kabul province,capital of Afghanistan.Two models,Analytical Hierarchy Process(AHP)and Analytical Network Process(ANP),were used to select suitable areas for establishing a solar power plant.The application of the proposed model has been made possible by integrating four constraints such as climate,environmental,topography,and economical which comprised twelve criteria:solar radiation,yearly average rainfall,land slope,aspect,land use,dust,geology and proximity to faults,main roads,Normalized difference vegetation index,urban areas river and water bodies.The findings indicate that there is no considerable difference between the results of both models since both models identified more than 20%of the total area of Kabul province in suitable classes.Outputs maps conclude that northern and southern parts of Kabul city and the eastern part of Kabul province came to the range of suitable areas.It can be concluded that Kabul province is a source of sufficient potential for producing solar electricity.The results of this study can support the plans of the Afghanistan government in solar energy production and the implementation of photovoltaic power plants.展开更多
基金supported by the National Key R&D Program of China(No.2022YFC3003502).
文摘The northern segment of the North-South Seismic Belt is characterized by intense crustal deformation,well-developed active tectonics,and frequent occurrences of strong earthquakes.Therefore,conducting a Probabilistic Seismic Hazard Analysis(PSHA)for this region is of significant importance for supporting seismic fortification in major engineering projects and formulating disaster prevention and mitigation policies.In this study,a composite seismic source model was constructed by integrating data on historical earthquakes,active faults,and paleoseismicity.Furthermore,a logic tree framework was employed to quantify epistemic uncertainties,enabling a systematic seismic hazard assessment of the region.To more accurately characterize the spatial heterogeneity of seismic activity,improvements were made to both the Circular Spatial Smoothing Model(CSSM)with a fixed radius and the Adaptive Spatial Smoothing Model(ASSM),with full consideration given to the spatiotemporal completeness of historical earthquake magnitudes.Regarding the CSSM,for scenarios involving small sample sizes in earthquake catalogs,the cross-validation method proposed in this study demonstrated higher robustness than the maximum likelihood method in determining the optimal correlation distance.Performance evaluation results indicate that while both models effectively characterize seismic activity,the ASSM exhibits superior overall predictive performance compared to the CSSM,owing to its ability to adaptively adjust the smoothing radius according to seismic density.Significant discrepancies were observed in the Peak Ground Acceleration(PGA)results calculated with a 10%probability of exceedance in 50 years across different combinations of seismic source models.The single spatially smoothed point-source model yielded a maximum PGA of approximately 0.52 g,with high-value areas concentrated near historical epicenters,thereby significantly underestimating the hazard associated with major fault zones.When combined with the simple fault-source model,the maximum PGA increased to 0.8 g,with high-value zones exhibiting a zonal distribution along faults;however,the risk remained underestimated for faults with low slip rates that are nevertheless approaching their recurrence cycles.Following the introduction of the time-dependent characteristic fault-source model,local PGA values for faults in the middle-to-late stages of their recurrence cycles increased by a factor of 2 to 7 compared to the single model.These results demonstrate that the characteristic fault-source model reasonably delineates the time-dependence of large earthquake recurrence,thereby providing a more accurate assessment of imminent seismic risks.By comprehensively applying the improved spatially smoothed pointsource model,the simple fault-source model,and the characteristic fault-source model,the following faults within the region were identified as having high seismic hazard:the Huangxianggou,Zhangxian,and Tianshui segments of the Xiqinling northern edge fault;the Maqin-Maqu segment of the Dongkunlun fault;the Longriqu fault;the Maoergai fault;the Elashan fault;the Riyueshan fault;the eastern segment of the Lenglongling fault;the Maxianshan segment of the Maxianshan northern Margin fault;and the Maomaoshan-Jinqianghe segment of the Laohushan-Maomaoshan fault.As these faults are located within seismic gaps or are approaching the recurrence periods of large earthquakes,they should be prioritized for current and future seismic monitoring as well as disaster prevention and mitigation efforts.
基金Under the auspices of the National Natural Science Foundation of China(No.42371222,41971167)Fundamental Scientific Research Funds of Central China Normal University(No.CCNU24ZZ120)。
文摘Owing to intensified globalization and informatization,the structures of the urban scale hierarchy and urban networks between cities have become increasingly intertwined,resulting in different spatial effects.Therefore,this paper analyzes the spatial interaction between urban scale hierarchy and urban networks in China from 2019 to 2023,drawing on Baidu migration data and employing a spatial simultaneous equation model.The results reveal a significant positive spatial correlation between cities with higher hierarchy and those with greater network centrality.Within a static framework,we identify a positive interaction between urban scale hierarchy and urban network centrality,while their spatial cross-effects manifest as negative neighborhood interactions based on geographical distance and positive cross-scale interactions shaped by network connections.Within a dynamic framework,changes in urban scale hierarchy and urban networks are mutually reinforcing,thereby widening disparities within the urban hierarchy.Furthermore,an increase in a city’s network centrality had a dampening effect on the population growth of neighboring cities and network-connected cities.This study enhances understanding of the spatial organisation of urban systems and offers insights for coordinated regional development.
基金supported by the Fujian Provincial Science and Technology Program“University-Industry Cooperation Project”(2024Y4015)National Key R&D Plan of Strategic International Scientific and Technological Innovation Cooperation Project(2018YFE0207800).
文摘The increasing frequency of extreme weather events raises the likelihood of forest wildfires.Therefore,establishing an effective fire prediction model is vital for protecting human life and property,and the environment.This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers.Using monthly grid data from 2006 to 2020,a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province,China.We compared the fitting performance of the logistic regression model(LRM),the generalized additive logistic model(GALM),and the spatial generalized additive logistic model(SGALM).The results indicate that SGALMs had the best fitting results and the highest prediction accuracy.Meteorological factors significantly impacted forest fires in Fujian Province.Areas with high fire incidence were mainly concentrated in the northwest and southeast.SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation.This model provides piecewise interpretations of forest wildfire occurrences,which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.
文摘Today,environmental studies based on satellite imagery are known as making valuable contributions to the dynamics and spatial prediction of sensitive or complex ecosystems such as wide protected areas and represent sustainable decision tools.The Pendjari and W Transboundary Reserves which constitute biodiversity reservoirs,habitats for wildlife conservation lack substantial investigations on the vegetation dynamics.Despite the protection measures they benefit from,these reserves remain dependent on climatic hazards that can influence their stability.The present study is innovative since it applied remote sensing techniques combinedwith climate records fromthe last thirty years to analyze the past dynamics of land use and climate changes to predict the future trends of the vegetation cover of the two national parks in Benin,as well as their peripheries.The methodology used remote sensing and Geographic Information System(GIS)techniques that allowed the supervised classification of Landsat images from 1985,2000 and 2015.Climatic data were combined in R software to identify the break periods for climatic parameters.Finally,the predictive vegetation cover for the year 2030 was made by combining vegetation and climatic data in the“Land Change Modeler”extension.Results show ten land use and land cover classes which are the agglomerations,mosaics of fields and fallows,water bodies,dense forests,gallery forests,clear forests and wooded savannahs,swamp forests and shrubby wooded savannahs,saxicolous savannahs and bare ground.The natural vegetation decreased from 90.85%in 1985 to 83.54% in 2000 then to 79.56% in 2015,representing a decline of 11.39%over a study period of 30 years.The analysis of the climatic curves revealed the presence of a break,meaning drought frequencies.Thepredictivemodeling showed that land use units projected up to the year 2030 are consistent with past trends,but with the continued expansion of fields and fallows(2%)instead of the natural vegetation.This study not only provides good insights useful in the sustainable management of the Biosphere Reserves but will also motivate many other researches towards such ecosystems.
文摘Pricing dynamics and volatility are accelerating the adoption of global cryptocurrency.Despite challenges,cryptocurrencies such as Bitcoin are gaining widespread acceptance,particularly in countries with unbanked populations,the lack of bank controls,and inflation.This study investigates the global patterns of cryptocurrency adoption using Generalized Linear Models and Spatial Autoregressive Models.This research introduces a novel perspective on global cryptocurrency adoption using spatial models.Our findings reveal that cryptocurrency adoption is significantly influenced by economic instability,infrastructure availability,and spatial dynamics,with higher adoption rates in countries with limited access to traditional financial systems.
文摘Picture is a means of objects’representation and inter-subject communication,where various ways of spatial modelling interact.Unlike arbitrary signs,picture not only represents something different from itself,but shows the represented objects for viewer’s perception.Therefore,the external modelling in form of a material bearer is connected always with internal modelling of depicted objects.Not only perceptual images,but also internal models of other psychical levels participate in creation and interpretation of the pictures.These are,on the one hand,images of the apperceptual and conceptual levels,where schemes of represented objects and their verbal interpretations are formed.On the other hand,the images of sensorial level participate also in the internal modelling.All these mental models interact differently by artists and viewers and influence organization of external pictorial models mediating their contacts.
文摘The study aims to investigate county-level variations of the COVID-19 disease and vaccination rate. The COVID-19 data was acquired from usafact.org, and the vaccination records were acquired from the Ohio vaccination tracker dashboard. GIS-based exploratory analysis was conducted to select four variables (poverty, black race, population density, and vaccination) to explain COVID-19 occurrence during the study period. Consequently, spatial statistical techniques such as Moran’s I, Hot Spot Analysis, Spatial Lag Model (SLM), and Spatial Error Model (SEM) were used to explain the COVID-19 occurrence and vaccination rate across the 88 counties in Ohio. The result of the Local Moran’s I analysis reveals that the epicenters of COVID-19 and vaccination followed the same patterns. Indeed, counties like Summit, Franklin, Fairfield, Hamilton, and Medina were categorized as epicenters for both COVID-19 occurrence and vaccination rate. The SEM seems to be the best model for both COVID-19 and vaccination rates, with R2 values of 0.68 and 0.70, respectively. The GWR analysis proves to be better than Ordinary Least Squares (OLS), and the distribution of R2 in the GWR is uneven throughout the study area for both COVID-19 cases and vaccinations. Some counties have a high R2 of up to 0.70 for both COVID-19 cases and vaccinations. The outcomes of the regression analyses show that the SEM models can explain 68% - 70% of COVID-19 cases and vaccination across the entire counties within the study period. COVID-19 cases and vaccination rates exhibited significant positive associations with black race and poverty throughout the study area.
文摘The rapid expansion of tobacco farming poses a significant threat to biodiversity in Yunnan Province,China,a region known for its rich biodiversity.This study aims to understand the trade-offs between tobacco farming and higher plant species diversity,and to identify priority counties for conservation.We employed an integrated approach combining species distribution modeling,GIS overlay analysis,and empirical spatial regression to em pirically assess the impact of tobacco farming intensity on biodiversity risk.Our findings reveal a compelling negative spatial correlation between tobacco farming expansion and higher plant species diversity.Specifically,southern counties in Wenshan and Honghe prefectures are major priority areas of conservation that exhibit signif icant spatial correlations between biodiversity risks and high tobacco farming intensity.Quantitatively,at county level,a 1%increase in tobacco farming area corresponds to a 0.094%decrease in endemic higher plant species richness across the entire province.These results underscore the need for targeted and region-specific regulations to mitigate biodiversity loss and promote sustainable development in Yunnan Province.The integrated approach used in this study provides a comprehensive assessment of the tobacco-biodiversity trade-offs,offering actionable insights for policymaking.
基金supported by the Jilin Scientific and Technological Development Program(20210101466JC).
文摘In this paper,we consider the following spatial Solow-Swan model with density-dependent motion■whereσ>0,α∈(0,1)andΩ⊂ℝn(n≥1)is a bounded domain with smooth boundary andϕ∈C3([0,∞)),ϕ(s)>0 for all s≥0.We prove that if■then there exists a unique time-globally classical solution(u,v)for all n≥1,such a solution is bounded and satisfies u≥0,v>0.Moreover,we show that the above solution will convergence to the steady state(1,1)exponentially in L^(∞)as t→∞.
基金Under the auspices of the National Natural Science Foundation of China(No.42571228,42401212)National Natural Science Foundation of Shandong(No.ZR2024MD022)。
文摘Green innovation is an important driving force for high-quality development and an important guarantee for the revitalization of the old industrial base in Northeast China.However,research on green innovation is still insufficient.Using the super-efficiency epsilon-based measure Malmquist model,kernel density estimation,and spatial econometric model,this study investigated the spatiotemporal evolution characteristics and influencing factors of green innovation efficiency(GIE)in Northeast China from 2005 to 2020.The results reveal that:1)The GIE in Northeast China has obvious phased characteristics,where 2005-2011 was a period of fluctuating decline while 2012-2020 was a period of fluctuating increase,reflecting the severe resource and environmental constraints faced by the green innovation process.2)The GIE in the Northeast China has a significant spatial dependence,which has not formed a relatively stable spatial club feature.The process for improving the GIE in the Northeast China in the future is still arduous and far off.3)The interweaving and mutual influence of nonequilibrium factors have led to the diversity and complexity of the spatiotemporal pattern evolution of GIE.Overall,the level of economic development and industrial structure has a positive effect,while foreign investment and industrial agglomeration have a negative effect.The direct effects of government regulation,resource endowment,science and technology,environmental regulation,and urbanization are not significant.The research conclusion of this article can provide important reference for the revitalization of Northeast China.
文摘To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.
基金supported by the National Natural Science Foundation of China(Grants No.42301226,42271209 and 42471199)the Fundamental Research Funds for the Central Universities(Grant No.2024CDJXY014)+2 种基金the Natural Science Foundation of Jiangxi Province(Grant No.20242BAB25170)Special Funds for Water Resources in Jiangxi Province(Science and Technology Projects)(Grant No.202425YBKT16)the Young Talent Cultivation and Innovation Fund Project of Nanchang University(Grant No.XX202506030028).
文摘Great Lakes Regions(GLRs)in China often confront landscape fragmentation,wetland degradation,and ecological resilience(ER)losses owing to extensive and intensive urbanization.In GLRs,however,the ER responses to urbanization remain unclear.This study explored the spatiotemporal evolution of ER and urbanization in five GLRs in China to analyze the ER dynamic patterns along center−lakeside−periphery gradient.The Spatial Durbin Model(SDM)and Panel Threshold Model(PTM)were combined to reveal the spillover and threshold effects of urbanization in five GLRs.The results indicate that the ER in five GLRs declined with a rate of 21%from 2000 to 2020.There was a clear“center-periphery”contraction trend with low ER areas primarily spreading to human activity-concentrated regions such as lakesides,riversides,and road networks.Driven by economic and land urbanization,the average urbanization level increased from 0.06 to 0.13,where lakesides,riversides,and road networks were key areas undergoing expansion.The urbanization showed a noticeable negative spatial spillover effect on ER.Away from central lakes,the negative impacts on ER exhibited a two-phase decrease with the threshold of 81 km.This study contributes to the understanding of human-environment interactions by examining the ecological resilience response process of GLRs under the impact of urbanization.Based on a multidimensional“center−lakeside−periphery”analytical model,this study provides a strategic framework for ecological construction in GLRs in China,promoting sustainable development and adaptive capacity in vulnerable areas.
基金supported by the National Natural Science Foundation of China(72573020,72103022).
文摘This study analyzes the evolution of China's green technology innovation cooperation network from 2011 to 2020,utilizing green patent application data.Employing a Spatial Durbin Model(SDM),we scrutinized the network's influence on urban carbon emissions,utilizing panel data encompassing 323 city nodes.Results show network expansion and a shift in central nodes from eastern coastal areas to interior cities,with Beijing,Shenzhen,Nanjing,and Shanghai consistently acting as key innovation hubs.A core-periphery structure emerged,clustering cities into high-and low-cooperation clusters.Core cities,particularly Beijing,which gain informational advantages by bridging non-overlapping nodes and exhibit distinct characteristics in terms of the structural hole indexes,reflecting their multifaceted roles within the network.SDM analysis indicates that the green technology innovation cooperation network has a significant positive impact on urban carbon reduction efforts.Specifically,degree centrality,closeness centrality,effective size,efficiency,and hierarchy of node cities exhibit a negative correlation with carbon emissions,suggesting that higher centrality and efficiency within the network correlate with lower emissions.Conversely,betweenness centrality and constraint have a positive impact on emissions,indicating that cities that act as bridges in the network may paradoxically contribute to higher emissions.Moreover,the network's influence on carbon emissions is nuanced across different green technology sectors.Cooperation in areas such as waste management,alternative energy production,energy conservation,agriculture and forestry,and transportation is found to have a more substantial impact on carbon reduction than cooperation in nuclear power,and administrative,regulatory,and design fields.
基金NGI’s financial support for this studyThe funding comes in from The Research Council of Norway。
文摘This paper introduces three machine learning(ML)algorithms,the‘ensemble'Random Forest(RF),the‘ensemble'Gradient Boosted Regression Tree(GBRT)and the Multi Layer Perceptron neural network(MLP)and applies them to the spatial modelling of shallow landslides near Kvam in Norway.In the development of the ML models,a total of 11 significant landslide controlling factors were selected.The controlling factors relate to the geomorphology,geology,geo-environment and anthropogenic effects:slope angle,aspect,plan curvature,profile curvature,flow accumulation,flow direction,distance to rivers,water content,saturation,rainfall and distance to roads.It is observed that slope angle was the most significant controlling factor in the ML analyses.The performance of the three ML models was evaluated quantitatively based on the Receiver Operating Characteristic(ROC)analysis.The results show that the‘ensemble'GBRT machine learning model yielded the most promising results for the spatial prediction of shallow landslides,with a 95%probability of landslide detection and 87%prediction efficiency.
文摘Engineering excavation GIS (E 2 GIS) is a real-3D GIS serving for geosciences related to geo-engineering, civil engineering and mining engineering based on generalized tri-prism (GTP) model. As two instances of GTP model, G\|GTP is used for the real\|3D modeling of subsurface geological bodies, and E\|GTP is used for the real\|3D modeling of subsurface engineering excavations.In the light of the discussions on the features and functions of E 2 GIS, the modeling principles of G\|GTP and E\|GTP are introduced. The two models couple together seamlessly to form an integral model for subsurface spatial objects including both geological bodies and excavations. An object\|oriented integral real\|3D data model and integral spatial topological relations are discussed.
文摘The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively population data with other data including natural resources, environment, society and economy, build 1km GRIDs of natural resources reserves per person, population density and other economic and environmental data, which are necessary to the national management and macro adjustment and control of natural resources and dynamic monitoring of population. In order to establish population information system serving national decision making, three steps ought to be followed:1) establishing complete geographical spatial data foundation infrastructure including the establishment of electric map of residence with high resolution using topographical map with large scale and high resolution satellite remote sensing data, the determination of attribute information of housing and office buildings, and creating complete set of attribute database and rapid data updating; 2) establishing complete census systems including improving the transformation efficiency from census data to digital database and strengthening the link of census database and geographical spatial database, meanwhile, the government should attach great importance to the establishment and integration of population migration database; 3) considering there is no GIS software specially serving the analysis and management of population data, a practical approach is to add special modules to present software system, which works as a bridge actualizing the digitization and spatialization of population geography research.
基金supported by the Chinese National Natural Science Fund Project(41072084)National Program on Key Basic Research Project(973 Program)(No. 2009CB219502-3)Prof.Qiu Yinan at RIPED(Research Institute for Petroleum Exploration and Development) of CNPC(China National Petroleum Corporation) for his belief in this work and financial support of the research
文摘Daihai Lake, a modern lacustrine rift basin, located in Inner Mongolia, North China, serves as an important modern analog for understanding deltaic depositional processes in an active rift setting. Two of the deltas （Yuanzigou delta and Bulianghe delta） on the margins of Daihai Lake were surveyed to compare and contrast stacking patterns using aerial photographs, field trenching and sediment sampling. Shallow cores and trench data collected from the margins of Daihai Lake indicate that a variety of depositional processes have been active since Daihai Lake formed. Two 3-D sedimentation models which employ chronostratigraphic correlation technique were generated. The chronostratigraphic sedimentation models predict and represent the architectures and sand-body continuity of sediments. Stratigraphical coincidence of the broad sheeted drifts and channel erosion suggests a coupling between downslope and alongslope processes. Distributary mouth bars are prevalent in the front of deltas on steeper slopes due to the dominance of down-slope flows. On the contrary, the along-slope currents favor the development of distal bar deposits with sheeted sandbodies on gentle depositional slopes. This study provides an insight into the architecture of complex sedimentary facies associated with highlighting key differences between downslope flows and alongslope currents. The distribution of sand within these deltas is of particular interests, with applications in understanding the architecture of hydrocarbon reservoirs formed in lacustrine rift basin.
基金Foundation： National Natural Science Foundation of China, No.41171328, No.41201184, No.41101537 National Basic Program of China, No.2010CB951502
文摘Understanding crop patterns and their changes on regional scale is a critical re- quirement for projecting agro-ecosystem dynamics. However, tools and methods for mapping the distribution of crop area and yield are still lacking. Based on the cross-entropy theory, a spatial production allocation model （SPAM） has been developed for presenting spa- tio-temporal dynamics of maize cropping system in Northeast China during 1980-2010. The simulated results indicated that （1） maize sown area expanded northwards to 48~N before 2000, after that the increased sown area mainly occurred in the central and southern parts of Northeast China. Meanwhile, maize also expanded eastwards to 127°E and lower elevation （less than 100 m） as well as higher elevation （mainly distributed between 200 m and 350 m）; （2） maize yield has been greatly promoted for most planted area of Northeast China, espe- cially in the planted zone between 42°N and 48°N, while the yield increase was relatively homogeneous without obvious longitudinal variations for whole region; （3） maize planting density increased gradually to a moderately high level over the investigated period, which reflected the trend of aggregation of maize cultivation driven by market demand.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.
文摘Energy planning and solar plant site selections are vital strategic decisions and one of the most complex executive challenges in the interconnected procedures.It is essential to study the potential renewable energy sources in Afghanistan to select the most sustainable sites for solar power production in populated cities.This study is based on the combination of a Geographic Information System,Remote sensing,and multi-criteria decision-making technique to evaluate the optimal placement of photovoltaic solar power plants in the Kabul province,capital of Afghanistan.Two models,Analytical Hierarchy Process(AHP)and Analytical Network Process(ANP),were used to select suitable areas for establishing a solar power plant.The application of the proposed model has been made possible by integrating four constraints such as climate,environmental,topography,and economical which comprised twelve criteria:solar radiation,yearly average rainfall,land slope,aspect,land use,dust,geology and proximity to faults,main roads,Normalized difference vegetation index,urban areas river and water bodies.The findings indicate that there is no considerable difference between the results of both models since both models identified more than 20%of the total area of Kabul province in suitable classes.Outputs maps conclude that northern and southern parts of Kabul city and the eastern part of Kabul province came to the range of suitable areas.It can be concluded that Kabul province is a source of sufficient potential for producing solar electricity.The results of this study can support the plans of the Afghanistan government in solar energy production and the implementation of photovoltaic power plants.
