Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation.Traditional methods require a long time to evaluate and rely heavily on human experience.There...Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation.Traditional methods require a long time to evaluate and rely heavily on human experience.Therefore,based on the key factors affecting landslides,this paper designs a geological disaster prediction model based on Monte Carlo neural network(MCNN).Firstly,based on the weights of evidence method,a correlation analysis was conducted on common factors affecting landslides,and several key factors that have the greatest impact on landslide disasters,including geological lithology,slope gradient,slope type,and rainfall,were identified.Then,based on the monitoring data of Lanzhou City,18367 data records were collected and collated to form a dataset.Subsequently,these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN.After determining the hyperparameters of the model,the training and prediction capabilities of the model were evaluated.Through comparison with several other artificial intelligence models,it was found that the prediction accuracy of the model studied in this paper reached 89%,and the Macro-Precision,Macro-Recall,and Macro-F1 indicators were also higher than other models.The area under curve(AUC)index reached 0.8755,higher than the AUC value based on a single influencing factor in traditional methods.Overall,the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.展开更多
Groundwater levels are gradually declining in basins around the world due to anthropogenic and natural factors.Climate is not the only factor contributing to change in groundwater levels,population growth and economic...Groundwater levels are gradually declining in basins around the world due to anthropogenic and natural factors.Climate is not the only factor contributing to change in groundwater levels,population growth and economic progress are leading to increased water demand.Areas used for agricultural irrigation are expanding,necessitating the use of artificial groundwater recharge as a method to sustain pumping and enhance storage.The present study delineates potential locations of significant groundwater resources that already exist using a geostatistical approach as a method to identify potential groundwater recharge zones.The Multi-Influencing Factors(MIF)technique was applied to determine the relationship between different landscape and climatic factors that influence groundwater recharge.Factors include topography,climate,hydrogeology,population,economic change,and geology.Integration of these factors enabled the identification of potential locations of groundwater suitable for artificial recharge efforts based on weights derived through the MIF technique.We applied these weights to derive a groundwater recharge index(GRI)map.The map was delineated into three groundwater recharge zones classified by their potential areal coverage as a metric for recharge suitability,namely low,medium and high suitability,occupying areas of 8625 km2(30.06%),9082 km2(31.65%),and 10,989 km~2(38.29%),respectively.Our findings have important implications for designing sustainable groundwater development and land-use plans for the coming century.展开更多
基金partially supported by National Key R&D Program of China(No.2020YFC0832500)Gansu Province Science and Technology Major Project—Industrial Project(No.22ZD6GA048)+6 种基金Gansu Province Key Research and Development Plan—Industrial Project(No.22YF7GA004)Fundamental Research Funds for the Central Universities(Nos.lzujbky-2022-kb12,lzujbky-2021-sp43,lzujbky-2020-sp02,lzujbky-2019-kb51,and lzujbky-2018-k12)Ministry of Education—China Mobile Research Foundation(No.MCM20170206)National Natural Science Foundation of China(No.61402210)Science and Technology Plan of Qinghai Province(No.2020-GX-164)Supercomputing Center of Lanzhou University,Google Research AwardsGoogle Faculty Award.
文摘Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation.Traditional methods require a long time to evaluate and rely heavily on human experience.Therefore,based on the key factors affecting landslides,this paper designs a geological disaster prediction model based on Monte Carlo neural network(MCNN).Firstly,based on the weights of evidence method,a correlation analysis was conducted on common factors affecting landslides,and several key factors that have the greatest impact on landslide disasters,including geological lithology,slope gradient,slope type,and rainfall,were identified.Then,based on the monitoring data of Lanzhou City,18367 data records were collected and collated to form a dataset.Subsequently,these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN.After determining the hyperparameters of the model,the training and prediction capabilities of the model were evaluated.Through comparison with several other artificial intelligence models,it was found that the prediction accuracy of the model studied in this paper reached 89%,and the Macro-Precision,Macro-Recall,and Macro-F1 indicators were also higher than other models.The area under curve(AUC)index reached 0.8755,higher than the AUC value based on a single influencing factor in traditional methods.Overall,the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.
文摘Groundwater levels are gradually declining in basins around the world due to anthropogenic and natural factors.Climate is not the only factor contributing to change in groundwater levels,population growth and economic progress are leading to increased water demand.Areas used for agricultural irrigation are expanding,necessitating the use of artificial groundwater recharge as a method to sustain pumping and enhance storage.The present study delineates potential locations of significant groundwater resources that already exist using a geostatistical approach as a method to identify potential groundwater recharge zones.The Multi-Influencing Factors(MIF)technique was applied to determine the relationship between different landscape and climatic factors that influence groundwater recharge.Factors include topography,climate,hydrogeology,population,economic change,and geology.Integration of these factors enabled the identification of potential locations of groundwater suitable for artificial recharge efforts based on weights derived through the MIF technique.We applied these weights to derive a groundwater recharge index(GRI)map.The map was delineated into three groundwater recharge zones classified by their potential areal coverage as a metric for recharge suitability,namely low,medium and high suitability,occupying areas of 8625 km2(30.06%),9082 km2(31.65%),and 10,989 km~2(38.29%),respectively.Our findings have important implications for designing sustainable groundwater development and land-use plans for the coming century.
