Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping - Bui, Quang-Thanh; Nguyen, Quoc-Huy; Nguyen, Xuan Linh; Pham, Vu Dong; Nguyen, Huu Duy; Pham, Van-Manh (Journal of hydrology (Amsterdam), 2020)
•Examination of hybrid models that combine several swarm intelligenceoptimizers with deep learning neural network.•Comparison of performance of proposed models with benchmarked classifiers and algorithms.•Potential application of proposed models in flood susceptibility mapping is investigated. This study proposed and compared several novel hybrid models that combined swarm intelligence algorithms and Deep Learning Neural Network for flood susceptibility mapping. Lai Chau, a province in the northwest mountainous region of Vietnam was chosen as a case study since it had recently undergone severe flashflood in 2018. For this purpose, numerical predictor variables such as topographically derived factors (Digital Elevation Model, Aspect, Slope, Curvature, Topographic Wetness Index), climatic variables (Rain), and hydrological variables (stream density, stream power index, distance to river) and multiple remote sensing indices (Normalized Difference Vegetation Index, Normalized Difference Buildup Index) were used. These predictor variables were selected because they are globally collectible and reproducible. The performances of these models were evaluated by using common statistical indicators, namely Root Mean Square Error, Mean Absolute Error, Overall Accuracy and Area under Receiving Operating Characteristics, and the statistical test of differences. The results showed that the proposed swarm intelligence models outperformed benchmarked methods, namely Particle Swarm Optimization, Support Vector Machine, Random Forest in almost all comparing indicators. It is suggested that proposed models are more robust than the classifiers, which were used for benchmarking and they are good alternatives for flood susceptibility mapping given the availability of dataset.