Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment
Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood po...
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| Veröffentlicht in: | Journal of environmental management Jg. 265; S. 110485 |
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| Hauptverfasser: | , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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England
Elsevier Ltd
01.07.2020
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| ISSN: | 0301-4797, 1095-8630, 1095-8630 |
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| Abstract | Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results.
[Display omitted]
•This study presents six novel ensembles used to identify the areas susceptible to floods.•Historical flood locations were considered into the methodological workflow.•The model performances were assessed through several statistical metrics.•Generally, more than 19% of the study area has a high and very high flood susceptibility. |
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| AbstractList | Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results. Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results. [Display omitted] •This study presents six novel ensembles used to identify the areas susceptible to floods.•Historical flood locations were considered into the methodological workflow.•The model performances were assessed through several statistical metrics.•Generally, more than 19% of the study area has a high and very high flood susceptibility. Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results.Across the world, the flood magnitude is expected to increase as well as the damage caused by their occurrence. In this case, the prediction of areas which are highly susceptible to these phenomena becomes very important for the authorities. The present study is focused on the evaluation of flood potential within Trotuș river basin in Romania using six ensemble models created by the combination of Analytical Hierarchy Process (AHP), Certainty Factor (CF) and Weights of Evidence (WOE) on one hand, and Gradient Boosting Trees (GBT) and Multilayer Perceptron (MLP) on the other hand. A number of 12 flood predictors, 172 flood locations and 172 non-flood locations were used. A percentage of 70% of flood and non-flood locations were used as input in models. From the input data, 70% were used as training sample and 30% as validating sample. The highest accuracy was obtained by the MLP-CF model in terms of both training (0.899) and testing (0.889) samples. A percentage between 21.88% and 36.33% of study area is covered with high and very high flood potential. The results validation, performed through the ROC Curve method, highlights that the MLP-CF model provided the most accurate results. |
| ArticleNumber | 110485 |
| Author | Vojtek, Matej Thao Nhi, Pham Thi Costache, Romulus Thuy Linh, Nguyen Thi Khoi, Dao Nguyen Dung, Tran Duc Lee, Sunmin Vojteková, Jana Avand, Mohammadtaghi Pham, Quoc Bao |
| Author_xml | – sequence: 1 givenname: Romulus surname: Costache fullname: Costache, Romulus organization: Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, 050663, Bucharest, Romania – sequence: 2 givenname: Quoc Bao surname: Pham fullname: Pham, Quoc Bao organization: Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam – sequence: 3 givenname: Mohammadtaghi surname: Avand fullname: Avand, Mohammadtaghi organization: Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, 14115-111, Iran – sequence: 4 givenname: Nguyen Thi surname: Thuy Linh fullname: Thuy Linh, Nguyen Thi organization: Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Viet Nam – sequence: 5 givenname: Matej surname: Vojtek fullname: Vojtek, Matej organization: Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, 94974, Nitra, Slovakia – sequence: 6 givenname: Jana surname: Vojteková fullname: Vojteková, Jana organization: Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Trieda A. Hlinku 1, 94974, Nitra, Slovakia – sequence: 7 givenname: Sunmin surname: Lee fullname: Lee, Sunmin organization: Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, South Korea – sequence: 8 givenname: Dao Nguyen surname: Khoi fullname: Khoi, Dao Nguyen organization: Faculty of Environment, University of Science, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam – sequence: 9 givenname: Pham Thi surname: Thao Nhi fullname: Thao Nhi, Pham Thi email: Phamtthaonhi2@duytan.edu.vn organization: Institute of Research and Development, Duy Tan University, Danang, 550000, Viet Nam – sequence: 10 givenname: Tran Duc surname: Dung fullname: Dung, Tran Duc organization: Center of Water Management and Climate Change, Institute for Environment and Resources, Vietnam National University – Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, Viet Nam |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32421551$$D View this record in MEDLINE/PubMed |
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