A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area.

By D. Tien Bui, Nhat-Duc Hoang, Francisco Martínez-Alvarez, P. T. Ngo, Pham Viet Hoa, T. Pham, Pijush Samui, Romulus Costache. Science of the Total Environment, 2019

361 citations recorded by the source corpus.

This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/428e9c6d065c0c491bba37fe14fe1628f2b74a24

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Citations in the corpus

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, background): “…et al. 2017; Liu et al. 2019c; Fan et al. 2019), speech recognition (Fayek et al. 2017; Cummins et al. 2018), time series and spatiotemporal prediction (Chen et al. 2018; Yang and Chen 2019; Xu et al. 2019; Bui et al. 2019) and natural language processing (Shin et al. 2017; Plappert et al. 2018).”