The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward...The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.展开更多
Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a sou...Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a source ALU using target ALU.The source ALUs and the target ALUs are all represented by the general ALUs and the mapping rules are applied in the algorithm.The mapping rules are described in a table fashion.The graph clustering algorithm is a branch and bound algorithm based on the graph formulation of the mapping algorithm.The mapping algorithm suits well mapping of regularly structured data path.Comparisons are made between the experimental results generated by 1 greedy algorithm and graphclustering algorithm,showing the feasibility of presented algorithm.展开更多
The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also inclu...The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also included to explain the effectiveness of the proposed methods. The results of this paper are interesting extensions of those known results.展开更多
Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were de...Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.展开更多
The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The ...The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.展开更多
With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation ...With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation capabilities have become one of the research hotspots.An accurate map construction is a prerequisite for a mobile robot to achieve autonomous localization and navigation.However,the problems of blurring and missing the borders of obstacles and map boundaries are often faced in the Gmapping algorithm when constructing maps in complex indoor environments.In this pursuit,the present work proposes the development of an improved Gmapping algorithm based on the sparse pose adjustment(SPA)optimizations.The improved Gmapping algorithm is then applied to construct the map of a mobile robot based on single-line Lidar.Experiments show that the improved algorithm could build a more accurate and complete map,reduce the number of particles required for Gmapping,and lower the hardware requirements of the platform,thereby saving and minimizing the computing resources.展开更多
A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the ex...A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.展开更多
The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed-point set. In this paper...The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed-point set. In this paper, we present new iterative algorithms for solving the split common fixed point problem of demimetric mappings in Hilbert spaces. Moreover, our algorithm does not need any prior information of the operator norm. Weak and strong convergence theorems are given under some mild assumptions. The results in this paper are the extension and improvement of the recent results in the literature.展开更多
We introduced a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings. Using the resolvent operator method in Hilbert spaces, we suggested a new proximal point algorithm for finding approx...We introduced a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings. Using the resolvent operator method in Hilbert spaces, we suggested a new proximal point algorithm for finding approximate solutions, which strongly converge to the exact solution of a fuzzy set-valued variational inclusion with (H,η)-monotone. The results improved and generalized the general quasi-variational inclusions with fuzzy set-valued mappings proposed by Jin and Tian Jin MM, Perturbed proximal point algorithm for general quasi-variational inclusions with fuzzy set-valued mappings, OR Transactions, 2005, 9(3): 31-38, (In Chinese); Tian YX, Generalized nonlinear implicit quasi-variational inclusions with fuzzy mappings, Computers & Mathematics with Applications, 2001, 42: 101-108.展开更多
Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio...Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.展开更多
With the Riemann solver to the scalar hyperbolic conservation law with a spatially varying flux, a δ-mapping algorithm was proposed. The algorithm and its prospective application in traffic flow problems were briefed...With the Riemann solver to the scalar hyperbolic conservation law with a spatially varying flux, a δ-mapping algorithm was proposed. The algorithm and its prospective application in traffic flow problems were briefed in the paper.展开更多
We provide an algorithm to classify the asymptotic sets of the dominant polynomial mappingsof degree 2, using the definition of the so-called “fa?ons” in [2]. We obtain a classification theorem for the asymptotic se...We provide an algorithm to classify the asymptotic sets of the dominant polynomial mappingsof degree 2, using the definition of the so-called “fa?ons” in [2]. We obtain a classification theorem for the asymptotic sets of dominant polynomial mappingsof degree 2. This algorithm can be generalized for the dominant polynomial mappingsof degree d, with any (n,d)∈(N*)2.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
An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map. Different from previous methods, this approach casts the stereo matching as a mu...An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map. Different from previous methods, this approach casts the stereo matching as a multi-extrema optimization problem such that finding the fittest solution from a set of potential disparity maps. Among a wide variety of optimization techniques, GAs are proven to be potentially effective methods for the global optimization problems with large search space. With this idea, each disparity map is viewed as an individual and the disparity values are encoded as chromosomes, so each individual has lots of chromosomes in the approach. Then, several matching constraints are formulated into an objective function, and GAs are used to search the global optimal solution for the problem. Furthermore, the coarse-to-fine strategy has been embedded in the approach so as to reduce the matching ambiguity and the time consumption. Finally, experimental results on synthetic and real images show the performance of the work.展开更多
The purpose of this paper is to study necessary and su？cient condition for the strong convergence of a new parallel iterative algorithm with errors for two finite families of uniformly L-Lipschitzian mappings in Bana...The purpose of this paper is to study necessary and su？cient condition for the strong convergence of a new parallel iterative algorithm with errors for two finite families of uniformly L-Lipschitzian mappings in Banach spaces. The results presented in this paper improve and extend the recent ones announced by [2–7].展开更多
Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard la...Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.展开更多
基金funded by the State Grid Science and Technology Project“Research on Key Technologies for Prediction and Early Warning of Large-Scale Offshore Wind Power Ramp Events Based on Meteorological Data Enhancement”(4000-202318098A-1-1-ZN).
文摘The development of wind power clusters has scaled in terms of both scale and coverage,and the impact of weather fluctuations on cluster output changes has become increasingly complex.Accurately identifying the forward-looking information of key wind farms in a cluster under different weather conditions is an effective method to improve the accuracy of ultrashort-term cluster power forecasting.To this end,this paper proposes a refined modeling method for ultrashort-term wind power cluster forecasting based on a convergent cross-mapping algorithm.From the perspective of causality,key meteorological forecasting factors under different cluster power fluctuation processes were screened,and refined training modeling was performed for different fluctuation processes.First,a wind process description index system and classification model at the wind power cluster level are established to realize the classification of typical fluctuation processes.A meteorological-cluster power causal relationship evaluation model based on the convergent cross-mapping algorithm is pro-posed to screen meteorological forecasting factors under multiple types of typical fluctuation processes.Finally,a refined modeling meth-od for a variety of different typical fluctuation processes is proposed,and the strong causal meteorological forecasting factors of each scenario are used as inputs to realize high-precision modeling and forecasting of ultra-short-term wind cluster power.An example anal-ysis shows that the short-term wind power cluster power forecasting accuracy of the proposed method can reach 88.55%,which is 1.57-7.32%higher than that of traditional methods.
文摘Register transfer level mapping (RTLM) algorithm for technology mapping at RT level is presented,which supports current design methodologies using high level design and design reuse.The mapping rules implement a source ALU using target ALU.The source ALUs and the target ALUs are all represented by the general ALUs and the mapping rules are applied in the algorithm.The mapping rules are described in a table fashion.The graph clustering algorithm is a branch and bound algorithm based on the graph formulation of the mapping algorithm.The mapping algorithm suits well mapping of regularly structured data path.Comparisons are made between the experimental results generated by 1 greedy algorithm and graphclustering algorithm,showing the feasibility of presented algorithm.
基金The NSF(11071053)of ChinaNatural Science Basic Research Plan(2014JM2-1003)in Shaanxi Province of ChinaScientific Research Project(YD2016-12)of Yan’an University
文摘The purpose of this article is to propose a new hybrid projection method for a quasi-nonexpansive mapping. The strong convergence of the algorithm is proved in real Hilbert spaces. A numerical experiment is also included to explain the effectiveness of the proposed methods. The results of this paper are interesting extensions of those known results.
基金Projects(60234030 60404021) supported by the National Natural Science Foundation of China
文摘Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.
基金The Natural Science Foundation of Jiangsu Province,China(No.BK20200470)China Postdoctoral Science Foundation(No.2021M691595)Innovation and Entrepreneurship Plan Talent Program of Jiangsu Province(No.AD99002).
文摘The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.
基金National Key Research and Development of China(No.2019YFB1600700)Sichuan Science and Technology Planning Project(No.2021YFSY0003)。
文摘With the rapid development in the service,medical,logistics and other industries,and the increasing demand for unmanned mobile devices,mobile robots with the ability of independent mapping,localization and navigation capabilities have become one of the research hotspots.An accurate map construction is a prerequisite for a mobile robot to achieve autonomous localization and navigation.However,the problems of blurring and missing the borders of obstacles and map boundaries are often faced in the Gmapping algorithm when constructing maps in complex indoor environments.In this pursuit,the present work proposes the development of an improved Gmapping algorithm based on the sparse pose adjustment(SPA)optimizations.The improved Gmapping algorithm is then applied to construct the map of a mobile robot based on single-line Lidar.Experiments show that the improved algorithm could build a more accurate and complete map,reduce the number of particles required for Gmapping,and lower the hardware requirements of the platform,thereby saving and minimizing the computing resources.
基金Sponsored by the Funds for Creative Research Groups of China(Grant No. 60821001)National Natural Science Foundation of China(Grant No.60973108 and 60902050)973 Project of China (Grant No.2007CB310703)
文摘A major challenge of network virtualization is the virtual network resource allocation problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. However, the existing algorithms are almost concentrated on the randomly small-scale network topology, which is not suitable for practical large-scale network environments, because more time is spent on traversing SN and VN, resulting in VN requests congestion. To address this problem, virtual network mapping algorithm is proposed for large-scale network based on small-world characteristic of complex network and network coordinate system. Compared our algorithm with algorithm D-ViNE, experimental results show that our algorithm improves the overall performance.
文摘The split common fixed point problem is an inverse problem that consists in finding an element in a fixed point set such that its image under a bounded linear operator belongs to another fixed-point set. In this paper, we present new iterative algorithms for solving the split common fixed point problem of demimetric mappings in Hilbert spaces. Moreover, our algorithm does not need any prior information of the operator norm. Weak and strong convergence theorems are given under some mild assumptions. The results in this paper are the extension and improvement of the recent results in the literature.
基金the Natural Science Foundation of China (No. 10471151)the Educational Science Foundation of Chongqing (KJ051307).
文摘We introduced a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings. Using the resolvent operator method in Hilbert spaces, we suggested a new proximal point algorithm for finding approximate solutions, which strongly converge to the exact solution of a fuzzy set-valued variational inclusion with (H,η)-monotone. The results improved and generalized the general quasi-variational inclusions with fuzzy set-valued mappings proposed by Jin and Tian Jin MM, Perturbed proximal point algorithm for general quasi-variational inclusions with fuzzy set-valued mappings, OR Transactions, 2005, 9(3): 31-38, (In Chinese); Tian YX, Generalized nonlinear implicit quasi-variational inclusions with fuzzy mappings, Computers & Mathematics with Applications, 2001, 42: 101-108.
基金supported by the National Natural Science Foundation of China(NSFC)under Grant No.62061024the Project of Gansu Province Science and Technology Department under Grant No.22ZD6GA055.
文摘Differential spatial modulation(DSM)is a multiple-input multiple-output(MIMO)transmission scheme.It has attracted extensive research interest due to its ability to transmit additional data without increasing any radio frequency chain.In this paper,DSM is investigated using two mapping algorithms:Look-Up Table Order(LUTO)and Permutation Method(PM).Then,the bit error rate(BER)performance and complexity of the two mapping algorithms in various antennas and modulation methods are verified by simulation experiments.The results show that PM has a lower BER than the LUTO mapping algorithm,and the latter has lower complexity than the former.
文摘With the Riemann solver to the scalar hyperbolic conservation law with a spatially varying flux, a δ-mapping algorithm was proposed. The algorithm and its prospective application in traffic flow problems were briefed in the paper.
文摘We provide an algorithm to classify the asymptotic sets of the dominant polynomial mappingsof degree 2, using the definition of the so-called “fa?ons” in [2]. We obtain a classification theorem for the asymptotic sets of dominant polynomial mappingsof degree 2. This algorithm can be generalized for the dominant polynomial mappingsof degree d, with any (n,d)∈(N*)2.
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.
文摘An approach to addressing the stereo correspondence problem is presented using genetic algorithms (GAs) to obtain a dense disparity map. Different from previous methods, this approach casts the stereo matching as a multi-extrema optimization problem such that finding the fittest solution from a set of potential disparity maps. Among a wide variety of optimization techniques, GAs are proven to be potentially effective methods for the global optimization problems with large search space. With this idea, each disparity map is viewed as an individual and the disparity values are encoded as chromosomes, so each individual has lots of chromosomes in the approach. Then, several matching constraints are formulated into an objective function, and GAs are used to search the global optimal solution for the problem. Furthermore, the coarse-to-fine strategy has been embedded in the approach so as to reduce the matching ambiguity and the time consumption. Finally, experimental results on synthetic and real images show the performance of the work.
基金supported by the National Natural Science Foun-dation of China (11071169)the Natural Science Foundation of Zhejiang Province (Y6110287)
文摘The purpose of this paper is to study necessary and su？cient condition for the strong convergence of a new parallel iterative algorithm with errors for two finite families of uniformly L-Lipschitzian mappings in Banach spaces. The results presented in this paper improve and extend the recent ones announced by [2–7].
基金The study was supported by College of Agriculture,Shiraz University(Grant No.96GRD1M271143).
文摘Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.
