Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran
Saeid Janizadeh,Mohammadtaghi Avand,Abolfazl Jaafari,Tran Van Phong,Mahmoud Bayat,Ebrahim Ahmadisharaf,Indra Prakash,Binh Thai Pham,Saro Lee +8 more
TLDR
In this article, the authors used five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA), to estimate flash flood susceptibility in the Tafresh watershed.Abstract:
Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.read more
Citations
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Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
TL;DR: It can be inferred that the machine learning models have higher LSP performance than general statistical and heuristic models due to its high AUC accuracy and reasonable LSIs distribution features, while general statistical model is limited by its linear analysis and heuristics limited by subjective weighting process.
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Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam,Swapan Talukdar,Susanta Mahato,Sonali Kundu,Kutub Uddin Eibek,Quoc Bao Pham,Alban Kuriqi,Nguyen Thi Thuy Linh +7 more
TL;DR: The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.
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A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
Dong Van Dao,Abolfazl Jaafari,Mahmoud Bayat,Davood Mafi-Gholami,Chongchong Qi,Hossein Moayedi,Tran Van Phong,Hai-Bang Ly,Tien-Thinh Le,Phan Trong Trinh,Chinh Luu,Nguyen Kim Quoc,Bui Nhi Thanh,Binh Thai Pham +13 more
TL;DR: A comparative analysis using the Wilcoxon signed-rank tests revealed a significant improvement of landslide prediction using the spatially explicit DL model over the quadratic discriminant analysis, Fisher's linear discriminantAnalysis, and multi-layer perceptron neural network.
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Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction
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TL;DR: The results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous and provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.
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Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
Phong Tung Nguyen,Duong Hai Ha,Mohammadtaghi Avand,Abolfazl Jaafari,Huu Duy Nguyen,Nadhir Al-Ansari,Tran Van Phong,Rohit Sharma,Raghvendra Kumar,Hiep Van Le,Lanh Si Ho,Indra Prakash,Binh Thai Pham +12 more
TL;DR: This study proposed four ensemble soft computing models based on logistic models for groundwater potential maps that would help in the management of groundwater storage resources and provide real-time information about groundwater quality.
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