GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models
Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/6/2039 |
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| author | Viet-Ha Nhu Saeid Janizadeh Mohammadtaghi Avand Wei Chen Mohsen Farzin Ebrahim Omidvar Ataollah Shirzadi Himan Shahabi John J. Clague Abolfazl Jaafari Fatemeh Mansoorypoor Binh Thai Pham Baharin Bin Ahmad Saro Lee |
| author_facet | Viet-Ha Nhu Saeid Janizadeh Mohammadtaghi Avand Wei Chen Mohsen Farzin Ebrahim Omidvar Ataollah Shirzadi Himan Shahabi John J. Clague Abolfazl Jaafari Fatemeh Mansoorypoor Binh Thai Pham Baharin Bin Ahmad Saro Lee |
| author_sort | Viet-Ha Nhu |
| collection | DOAJ |
| description | Gully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision−recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran. |
| first_indexed | 2024-12-19T23:01:26Z |
| format | Article |
| id | doaj.art-15fd42ee3a374897bade83c004cd3b5a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-19T23:01:26Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-15fd42ee3a374897bade83c004cd3b5a2022-12-21T20:02:29ZengMDPI AGApplied Sciences2076-34172020-03-01106203910.3390/app10062039app10062039GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining ModelsViet-Ha Nhu0Saeid Janizadeh1Mohammadtaghi Avand2Wei Chen3Mohsen Farzin4Ebrahim Omidvar5Ataollah Shirzadi6Himan Shahabi7John J. Clague8Abolfazl Jaafari9Fatemeh Mansoorypoor10Binh Thai Pham11Baharin Bin Ahmad12Saro Lee13Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 758307, VietnamDepartment of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, IranDepartment of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, P.O. Box 14115-111, IranCollege of Geology & Environment, Xi’an University of Science and Technology, Xi’an 710054, ChinaDepartment of Forestry, Range and Watershed Management, Faculty of Agriculture and Natural Resources, Yasouj University, Yasouj 75918-74934, IranDepartment of Rangeland and Watershed Management, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan 87317-53153, IranDepartment of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, IranDepartment of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Sanandaj 66177-15175, IranDepartment of Earth Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, CanadaResearch Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), Tehran P.O. Box 64414-356, IranData Mining Laboratory, Department of Engineering, College of Farabi, University of Tehran, Tehran 37181-17469, IranInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, MalaysiaGeoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, KoreaGully erosion destroys agricultural and domestic grazing land in many countries, especially those with arid and semi-arid climates and easily eroded rocks and soils. It also generates large amounts of sediment that can adversely impact downstream river channels. The main objective of this research is to accurately detect and predict areas prone to gully erosion. In this paper, we couple hybrid models of a commonly used base classifier (reduced pruning error tree, REPTree) with AdaBoost (AB), bagging (Bag), and random subspace (RS) algorithms to create gully erosion susceptibility maps for a sub-basin of the Shoor River watershed in northwestern Iran. We compare the performance of these models in terms of their ability to predict gully erosion and discuss their potential use in other arid and semi-arid areas. Our database comprises 242 gully erosion locations, which we randomly divided into training and testing sets with a ratio of 70/30. Based on expert knowledge and analysis of aerial photographs and satellite images, we selected 12 conditioning factors for gully erosion. We used multi-collinearity statistical techniques in the modeling process, and checked model performance using statistical indexes including precision, recall, F-measure, Matthew correlation coefficient (MCC), receiver operatic characteristic curve (ROC), precision−recall graph (PRC), Kappa, root mean square error (RMSE), relative absolute error (PRSE), mean absolute error (MAE), and relative absolute error (RAE). Results show that rainfall, elevation, and river density are the most important factors for gully erosion susceptibility mapping in the study area. All three hybrid models that we tested significantly enhanced and improved the predictive power of REPTree (AUC=0.800), but the RS-REPTree (AUC= 0.860) ensemble model outperformed the Bag-REPTree (AUC= 0.841) and the AB-REPTree (AUC= 0.805) models. We suggest that decision makers, planners, and environmental engineers employ the RS-REPTree hybrid model to better manage gully erosion-prone areas in Iran.https://www.mdpi.com/2076-3417/10/6/2039gully erosionwatershed managementmachine learninghybrid modelsgisiran |
| spellingShingle | Viet-Ha Nhu Saeid Janizadeh Mohammadtaghi Avand Wei Chen Mohsen Farzin Ebrahim Omidvar Ataollah Shirzadi Himan Shahabi John J. Clague Abolfazl Jaafari Fatemeh Mansoorypoor Binh Thai Pham Baharin Bin Ahmad Saro Lee GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models Applied Sciences gully erosion watershed management machine learning hybrid models gis iran |
| title | GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models |
| title_full | GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models |
| title_fullStr | GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models |
| title_full_unstemmed | GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models |
| title_short | GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models |
| title_sort | gis based gully erosion susceptibility mapping a comparison of computational ensemble data mining models |
| topic | gully erosion watershed management machine learning hybrid models gis iran |
| url | https://www.mdpi.com/2076-3417/10/6/2039 |
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