A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

作者：Khosravi, Khabat; Binh Thai Pham^*; Chapi, Kamran; Shirzadi, Ataollah; Shahabi, Himan; Revhaug, Inge; Prakash, Indra; Dieu Tien Bui

来源：Science of the Total Environment, 2018, 627: 744-755.

DOI：10.1016/j.scitotenv.2018.01.266

• Flood susceptibility assessment
• Machine learning
• Alternating Decision Trees
• Logistic Model Trees
• Reduced Error Pruning Trees
• Naive Bayes Trees

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naive Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank testswere used to validate and compare the prediction capability of the models. Results showthat the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas.

• 出版日期2018-6-15