B. Choubin, Omid Rahmati, Seyed Masoud Soleimanpour, S. Shadfar, Ahmad Najafi Igdir
{"title":"通过包裹法研究影响沟蚀发生的最重要的水环境驱动因素","authors":"B. Choubin, Omid Rahmati, Seyed Masoud Soleimanpour, S. Shadfar, Ahmad Najafi Igdir","doi":"10.1109/SACI58269.2023.10158570","DOIUrl":null,"url":null,"abstract":"This study aimed to draw connections between gully erosion occurrences and hydro-environmental factors in watershed areas that illustrate relationships. For this aim, feature elimination methods help diagnose the most key drivers. So, three Wrapper methods of Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), and Exhaustive Feature Selection (EFS) were taken to account for feature extraction, by considering the Random Forest model as an estimator. Considering the results of the models, the most important identified variables were aspect, drainage density, elevation, distance to road, landuse, and slope. The outcomes of this research procure insights for watershed managers to know how should face gully erosion.","PeriodicalId":339156,"journal":{"name":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Most Important Hydro-Environmental Drivers Affecting Gully Erosion Occurrence Through Wrapper Methods\",\"authors\":\"B. Choubin, Omid Rahmati, Seyed Masoud Soleimanpour, S. Shadfar, Ahmad Najafi Igdir\",\"doi\":\"10.1109/SACI58269.2023.10158570\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aimed to draw connections between gully erosion occurrences and hydro-environmental factors in watershed areas that illustrate relationships. For this aim, feature elimination methods help diagnose the most key drivers. So, three Wrapper methods of Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), and Exhaustive Feature Selection (EFS) were taken to account for feature extraction, by considering the Random Forest model as an estimator. Considering the results of the models, the most important identified variables were aspect, drainage density, elevation, distance to road, landuse, and slope. The outcomes of this research procure insights for watershed managers to know how should face gully erosion.\",\"PeriodicalId\":339156,\"journal\":{\"name\":\"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SACI58269.2023.10158570\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI58269.2023.10158570","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Most Important Hydro-Environmental Drivers Affecting Gully Erosion Occurrence Through Wrapper Methods
This study aimed to draw connections between gully erosion occurrences and hydro-environmental factors in watershed areas that illustrate relationships. For this aim, feature elimination methods help diagnose the most key drivers. So, three Wrapper methods of Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), and Exhaustive Feature Selection (EFS) were taken to account for feature extraction, by considering the Random Forest model as an estimator. Considering the results of the models, the most important identified variables were aspect, drainage density, elevation, distance to road, landuse, and slope. The outcomes of this research procure insights for watershed managers to know how should face gully erosion.