Deep learning neural networks for spatially explicit prediction of flash flood probability

Panahi, Mahdi; Jaafari, Abolfazl; Shirzadi, Ataollah; Shahabi, Himan; Rahmati, Omid; Omidvar, Ebrahim; Lee, Saro; Bui, Dieu Tien

doi:10.1016/j.gsf.2020.09.007

Cited by 102 publications

(67 citation statements)

References 61 publications

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“…Land use is a significant variable in research focusing on flood susceptibility modeling (Panahi et al, 2021) Land use is a major variable in research focusing on flood vulnerability modeling (Panahi et al, 2021). Land usage in the region has an impact on run-off and infiltration, resulting in floods.…”

Section: Lan Dusementioning

confidence: 99%

“…Nowadays, spatial modeling is done using Machine Learning (ML) approach for flood susceptibility modeling due to their high efficiency, accuracy, and predictability (Ahmadlou et al, 2019). ML algorithms often used for flood susceptibility modeling include Support Vector Machine (Tehrany et al, 2014), Logistic Regression (Pham et al, 2020), Artificial Neural Network (R. Costache et al, 2020), Bayesian Logistic Regression (Vogel et al, 2014), Decision Tree, Random Forest, Alternating Decision Tree, Logistic Model Tree, Naïve Bayes tree (Pham et al, 2020), Reduced Error Pruning Tree (W. Chen et al, 2019), k-nearest neighbor, and Deep Learning Neural Networks (Panahi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Luu

Nguyen

Amiri

et al. 2021

Vietnam J. Earth Sci.

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Floods are among the most frequent highly disastrous hazards affecting life, property, and the environment worldwide. While various models are available to predict flood susceptibility, no model is accurate enough to be used for all flood-prone areas. Model development using different algorithms is a continuous process to improve the prediction accuracy of flood susceptibility. In the study, we used the Radial Basis Function and Fisher’s linear discriminant function to develop a flood susceptibility map for a case study of Quang Binh Province. The model development used ten variables (elevation, slope, curvature, river density, distance from river, geomorphology, land use, flow accumulation, flow direction, and rainfall). For model training and validation, input data was split into a 70:30 ratio according to flood locations. Statistical indexes were used to evaluate model performance such as Receiver Operating Characteristic, the Area Under the ROC Curve, Root Mean Square Error, Accuracy, Sensitivity, Specificity, and Kappa index. Results indicated that the radial basis function classifier model had better performance in predicting flood susceptible areas based on the statistical measures (PPV = 92.00%, NPV = 87.00%, SST = 87.62%, SPF = 91.58%, ACC = 89.50%, Kappa = 0.790, MAE = 0.204, RMSE = 0.292 and AUC = 0.957. Therefore, the radial basis function classifier algorithm model is appropriate for predicting flood susceptibility in Quang Binh Province.

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Section: Lan Dusementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Luu

Nguyen

Amiri

et al. 2021

Vietnam J. Earth Sci.

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“…Recently, deep learning (DL) technique, a part of machine learning, is gradually applied in various fields. For example, Panahi (2020) used convolutional neural networks and recurrent neural networks to predict the probability of flash flood (Panahi et al, 2020); Kumar (2020) used deep learning model to complete the prediction of ground water depth (Kumar et al, 2020); Benzekri (2020) employed the deep learning model to construct an early forest fire detection system (Benzekri et al, 2020). In general, DL model performed a satisfactory ability of classification and regression.…”

Section: Instructionmentioning

confidence: 99%

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

Zhang¹,

Wang

Chen³

et al. 2021

Preprint

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The losses and damage caused by landslides are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were respectively prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.

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“…Morocco is not an exception, it has faces also innumerable flash flood events, the most important are Oued Ourika floods on 1987,1989and 1995(Atlas et al, 2014 , those of Mohammedia on 1996, 2002(Chaabane et al, 2017 , Guelmim region floods on 2002,2010 and 2014 (Talha et al, 2019) and the last event was in Tetouan on March 1st, 2021 . Nowadays, the vulnerability of flash floods is very high, it is necessary to think systematically about risk management (Panahi et al, 2021), starting with understanding the elements that influence the increase of this disaster, and then identifying flood prone areas. Many researchers strive to achieve these goals using several methods, starting with older method based on simple statistics such as frequency ratio, weight of evidence (Shafapour Tehrany et al, 2017 ;Talha et al, 2019 ;Radwan et al, 2019 ;Swain et al, 2020), and ending with artificial intelligence , machine learning approach (Ma et al, 2019 ;Arabameri et al, 2020Costache et al, 2020Elmahdy et al, 2020 ;Costache et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Performance of Machine Learning Algorithms for Mapping and Forecasting of Flash Flood Susceptibility in Tetouan, Morocco

Sellami

Maanan

Rhinane

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Since the industrial revolution, the world is experiencing a huge change in its climate, which causes many imbalances such as flash floods (FF). The aim of this study is to propose a new approach for detection and forecasting of flash flood susceptibility in the city of Tetouan, Morocco. For this regard, support vector machine (SVM), logistic regression (LR), random forest (RF), Naïve Bayes (NB) and Artificial neural network (ANN) are used based on 1101 points (680 flood points and 421 non-flood points) and 9 flash-flood predictors (Elevation , Slope , Aspect , LU/LC , Stream Power Index , Plan curvature , Profile Curvature , Topographic Position Index and Topographic Wetness Index ) that were extracted from the DEM (10m resolution) and satellite imagery (Sentinel 2B) of the study area . Models were trained on 70% and tested on 30% of this dataset also they were evaluated using several metrics such as the Receiver Operating Characteristic (ROC) Curve, precision, recall, score and kappa index. The result demonstrated that RF (AUC = 0.99, Accuracy = 96%, Kappa statistics = 0.92) has the highest performance, followed by ANN (AUC = 0.98, Accuracy = 95%, Kappa statistics = 0.89) and SVM (AUC = 0.96, Accuracy = 92%, Kappa statistics = 0.80). The proposed approach is an effective tool for forecasting and predicting FF that can help reduce the severity of this disaster.

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Deep learning neural networks for spatially explicit prediction of flash flood probability

Cited by 102 publications

References 61 publications

Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

Performance of Machine Learning Algorithms for Mapping and Forecasting of Flash Flood Susceptibility in Tetouan, Morocco

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