In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic informatio...In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.展开更多
The present paper summarizes a joint effort undertaken by the Instituto Geográfico Militar de Chile (IGM) and the Instituto Antártico Chileno (INACH) in order to obtain digital cartography of the Fildes Peni...The present paper summarizes a joint effort undertaken by the Instituto Geográfico Militar de Chile (IGM) and the Instituto Antártico Chileno (INACH) in order to obtain digital cartography of the Fildes Peninsula, Rey Jorge Island, Antarctica. This peninsula constitutes the prototype project area for the main IGM-INACH project No 153 “Cartographic Survey and Geographic Information System of the South Shetlands Islands” The Digital Cartography was implemented at the 1:5 000 scale, using geodetic GPS control points referenced to ITRF 92 and WGS 84 Data. The UTM Projection was used. All products were produced in compliance with the cartographic standards of the IGM. This cartography was designed in order to satisfy the requirements of a Geographic Information System developed by INACH. This geo-referenced database incorporates a variety of thematic information, enabling it to support scientific investigations, environmental and multi-disciplinary studies, and other applications. As a result of this project the Instituto Geográfico Militar de Chile produced a map at 1:5 000 scale in digital format, and also a 1:10 000 topographic map, in paper format, with two editions: first edition of two charts and a second edition with one chart covering the whole project area. Chile and other countries have a number of important permanent activities in this area. These maps are designed to support several and diverse geo-spatial studies related to these activities.展开更多
基金supported by the Basic Research Project of the Korea Institute of Geoscience and Mineral Resources(KIGAM)Project of Environmental Business Big Data Platform and Center Construction funded by the Ministry of Science and ICT。
文摘In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.
文摘The present paper summarizes a joint effort undertaken by the Instituto Geográfico Militar de Chile (IGM) and the Instituto Antártico Chileno (INACH) in order to obtain digital cartography of the Fildes Peninsula, Rey Jorge Island, Antarctica. This peninsula constitutes the prototype project area for the main IGM-INACH project No 153 “Cartographic Survey and Geographic Information System of the South Shetlands Islands” The Digital Cartography was implemented at the 1:5 000 scale, using geodetic GPS control points referenced to ITRF 92 and WGS 84 Data. The UTM Projection was used. All products were produced in compliance with the cartographic standards of the IGM. This cartography was designed in order to satisfy the requirements of a Geographic Information System developed by INACH. This geo-referenced database incorporates a variety of thematic information, enabling it to support scientific investigations, environmental and multi-disciplinary studies, and other applications. As a result of this project the Instituto Geográfico Militar de Chile produced a map at 1:5 000 scale in digital format, and also a 1:10 000 topographic map, in paper format, with two editions: first edition of two charts and a second edition with one chart covering the whole project area. Chile and other countries have a number of important permanent activities in this area. These maps are designed to support several and diverse geo-spatial studies related to these activities.
