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

Costache, Romulus; Ngo, Phuong Thao Thi; Bui, Dieu Tien

doi:10.3390/w12061549

Cited by 59 publications

(24 citation statements)

References 60 publications

(88 reference statements)

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“…In the present study, with the help of the FFPI method, we provided a way to determine floods in hydrologically unmonitored areas, thus covering a dysfunction mentioned in numerous studies that aimed to model the risk of flooding (Costache 2019b;Costache et al 2020;Minea 2013;Popa et al 2020;Zeng et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Flash Flood Assessment Using GIS and the Frequency Ratio Bivariate Statistical Model – Case Study, Codlea, Romania

Vasile¹,

Mehedinți²,

Popa³

et al. 2021

Air and Water Components of the Environment Conference

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Floods are one of the biggest natural disasters, causing significant economic and human losses. Regardless of the degree of development of urban or rural systems, floods account for about a third of all-natural disasters globally. Identifying areas vulnerable to floods is essential for better management and mitigation of their effects. The research aims to identify areas vulnerable to floods in the city of Codlea, Brașov County. Annually, the city records significant floods, one of the determining factors being its location, near the southern slope of the Perșani Mountains. The research proposes the Flash Flood Potential Index (FFPI) computation by combining GIS techniques with the Frequency Ration bivariate statistical model. The correlation of various flash-flood conditioning variables allowed us to compute the FFPI. The methodological approach could represent an essential tool for local authorities for better flood risk management.

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Section: Discussionmentioning

confidence: 99%

Flash Flood Assessment Using GIS and the Frequency Ratio Bivariate Statistical Model – Case Study, Codlea, Romania

Vasile¹,

Mehedinți²,

Popa³

et al. 2021

Air and Water Components of the Environment Conference

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“…Remote Sens. 2021, 13, x FOR PEER REVIEW 3 that have made contributions in this regard [27,28]. The common conclusion of the a studies of hybrid models is that these hybrid models have improved flood suscept prediction capabilities.…”

Section: Materials 21 Study Areamentioning

confidence: 99%

“…Tehrany et al integrated the weights-of-evidence and four kernel types of SVM in flood susceptibility mapping in Terengganu, and found that the WoE-RBF-SVM showed the best performance [26]. In addition, there are a lot of studies that have made contributions in this regard [27,28]. The common conclusion of the above studies of hybrid models is that these hybrid models have improved flood susceptibility prediction capabilities.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

et al. 2021

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Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.

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“…Along with its high support for flood and flash-flood early warning systems, the high-accurate estimation of flood exposed areas can also help to draw up the river basins flood defence plans (Albano et al 2017;Johann and Leismann 2017;Brillinger et al 2020). The following models are among the most popular ML algorithms used to estimate the flood susceptibility: Artificial Neural Network (ANN) (Costache et al 2020b), support vector machine (SVM) (Nguyen et al 2017;Tehrany et al 2019;Sahana et al 2020), decision trees-based models (Chen et al 2018;Khosravi et al 2018;Costache 2019c), naïve Bayes (Ali et al 2020;Tang et al 2020), deep learning neural network (Costache et al 2020a), extreme learning machine (Tang et al 2020). All the models applied in the above works achieved an accuracy higher than 80%.…”

Section: Introductionmentioning

confidence: 99%

Detection of areas prone to flood risk using state-of-the-art machine learning models

Costache

Arabameri

Elkhrachy

et al. 2021

Geomatics, Natural Hazards and Risk

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The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC ¼ 0.996).

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

Cited by 59 publications

References 60 publications

Flash Flood Assessment Using GIS and the Frequency Ratio Bivariate Statistical Model – Case Study, Codlea, Romania

Flash Flood Assessment Using GIS and the Frequency Ratio Bivariate Statistical Model – Case Study, Codlea, Romania

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Detection of areas prone to flood risk using state-of-the-art machine learning models

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