TY - JOUR
T1 - A novel deep learning neural network approach for predicting flash flood susceptibility
T2 - a case study at a high frequency tropical storm area
AU - Tien Bui, Dieu
AU - Hoang, Nhat-Duc
AU - Martínez-Álvarez, Francisco
AU - Ngo, Phuong-Thao Thi
AU - Hoa, Pham Viet
AU - Pham, Tien Dat
AU - Samui, Pijush
AU - Costache, Romulus
PY - 2020/1/20
Y1 - 2020/1/20
N2 - [Graphical abstract presents]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.
AB - [Graphical abstract presents]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.
KW - Flash flood
KW - Deep learning
KW - Adaptive moment estimation
KW - Geographic Information System (GIS)
KW - Vietnam
UR - http://www.scopus.com/inward/record.url?scp=85074477035&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2019.134413
DO - 10.1016/j.scitotenv.2019.134413
M3 - Article
C2 - 31706212
VL - 701
SP - 1
EP - 12
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 134413
ER -