Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India
Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree...
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MDPI AG
2020-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/5/1313 |
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| author | Sunil Saha Jagabandhu Roy Alireza Arabameri Thomas Blaschke Dieu Tien Bui |
| author_facet | Sunil Saha Jagabandhu Roy Alireza Arabameri Thomas Blaschke Dieu Tien Bui |
| author_sort | Sunil Saha |
| collection | DOAJ |
| description | Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions. |
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| language | English |
| last_indexed | 2024-04-11T22:18:56Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-346a4d90b5854624b60c9d30f3452e012022-12-22T04:00:15ZengMDPI AGSensors1424-82202020-02-01205131310.3390/s20051313s20051313Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern IndiaSunil Saha0Jagabandhu Roy1Alireza Arabameri2Thomas Blaschke3Dieu Tien Bui4Department of Geography, University of Gour Banga, Malda, West Bengal 732103, IndiaResearch Scholar, Dept. of Geography, University of Gour Banga, Malda, West Bengal 732103, IndiaDepartment of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, IranDepartment of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, AustriaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamGully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.https://www.mdpi.com/1424-8220/20/5/1313random forest (rf)gradient boosted regression tree (gbrt)tree ensemble (te)naïve bayes tree (nbt)r programming languagegeographical information system (gis) |
| spellingShingle | Sunil Saha Jagabandhu Roy Alireza Arabameri Thomas Blaschke Dieu Tien Bui Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India Sensors random forest (rf) gradient boosted regression tree (gbrt) tree ensemble (te) naïve bayes tree (nbt) r programming language geographical information system (gis) |
| title | Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India |
| title_full | Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India |
| title_fullStr | Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India |
| title_full_unstemmed | Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India |
| title_short | Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India |
| title_sort | machine learning based gully erosion susceptibility mapping a case study of eastern india |
| topic | random forest (rf) gradient boosted regression tree (gbrt) tree ensemble (te) naïve bayes tree (nbt) r programming language geographical information system (gis) |
| url | https://www.mdpi.com/1424-8220/20/5/1313 |
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