Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm

• 作者: Himan Shahabi Ataollah Shirzadi Somayeh Ronoud Shahrokh Asadi Binh Thai Pham Fatemeh Mansouripour Marten Geertsema John J.Clague Dieu Tien Bui
• 来源期刊: 地学前缘（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Research Paper
• 页码范围: 146-168
• 页数: 23页
• 语种: 英文

摘要

Flash floods are responsible for loss of life and considerable property damage in many countries. Flood suscepti-bility maps contribute to flood risk reduction in areas that are prone to this hazard if appropriately used by land-use planners and emergency managers. The main objective of this study is to prepare an accurate flood suscep-tibility map for the Haraz watershed in Iran using a novel modeling approach (DBPGA) based on Deep Belief Net-work (DBN) with Back Propagation (BP) algorithm optimized by the Genetic Algorithm (GA). For this task, a database comprising ten conditioning factors and 194 flood locations was created using the One-R Attribute Eval-uation (ORAE) technique. Various well-known machine learning and optimization algorithms were used as benchmarks to compare the prediction accuracy of the proposed model. Statistical metrics include sensitivity, specificity accuracy, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC) were used to assess the validity of the proposed model. The result shows that the proposed model has the highest goodness-of-fit (AUC=0.989) and prediction accuracy (AUC=0.985), and based on the validation dataset it outperforms benchmark models including LR (0.885), LMT (0.934), BLR (0.936), ADT (0.976), NBT (0.974), REPTree (0.811), ANFIS-BAT (0.944), ANFIS-CA (0.921), ANFIS-IWO (0.939), ANFIS-ICA (0.947), and ANFIS-FA (0.917). We conclude that the DBPGA model is an excellent alternative tool for predicting flash flood susceptibility for other regions prone to flash floods.