scholarly journals A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 239 ◽  
Author(s):  
Binh Thai Pham ◽  
Tran Van Phong ◽  
Huu Duy Nguyen ◽  
Chongchong Qi ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Logistic Model ◽  
Flash Flood ◽  
Kernel Logistic Regression ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
Logistic Model Tree ◽  
Flood Susceptibility Mapping

Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

scholarly journals Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

2020 ◽  
Vol 9 (12) ◽  
pp. 748
Author(s):  
Yifan Cao ◽  
Hongliang Jia ◽  
Junnan Xiong ◽  
Weiming Cheng ◽  
Kun Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Flash Floods ◽  
Ensemble Model ◽  
Susceptibility Assessment ◽  
Certainty Factor ◽  
Fujian Province ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

Permanent disability classification by combining evolutionary Generalized Radial Basis Function and logistic regression methods

2012 ◽  
Vol 39 (9) ◽  
pp. 8350-8355 ◽  
Author(s):  
A. Castaño ◽  
Francisco Fernández-Navarro ◽  
P.A. Gutiérrez ◽  
César Hervás-Martínez
Keyword(s):  
Logistic Regression ◽  
Radial Basis Function ◽  
Basis Function ◽  
Permanent Disability ◽  
Regression Methods ◽  
Radial Basis

scholarly journals Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model

2019 ◽  
Vol 11 (13) ◽  
pp. 1589 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
Himan Shahabi ◽  
Prasad Daggupati ◽  
Jan F. Adamowski ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vegetation Index ◽  
Slope Angle ◽  
Training Data ◽  
Multivariate Logistic Regression Model ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Validation Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

scholarly journals Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1549 ◽  
Author(s):  
Romulus Costache ◽  
Phuong Thao Thi Ngo ◽  
Dieu Tien Bui
Keyword(s):  
Neural Network ◽  
Operating Characteristic ◽  
Flash Flood ◽  
Flood Susceptibility ◽  
Statistical Measures ◽  
Geospatial Database ◽  
Machine Leaning ◽  
Flood Susceptibility Mapping ◽  
Deep Learning Neural Network ◽  
Hierarchy Process

This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.

scholarly journals Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran

2019 ◽  
Vol 11 (19) ◽  
pp. 5426 ◽  
Author(s):  
Saeid Janizadeh ◽  
Mohammadtaghi Avand ◽  
Abolfazl Jaafari ◽  
Tran Van Phong ◽  
Mahmoud Bayat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flash Flood ◽  
Susceptibility Mapping ◽  
Annual Rainfall ◽  
Slope Aspect ◽  
Flood Events ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping

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.

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