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

By Khabat Khosravi, Binh Thai Pham, Kamran Chapi, Ataollah Shirzadi, Himan Shahabi, Inge Revhaug, Indra Prakash, D. Tien Bui. Science of the Total Environment, 2018

676 citations recorded by the source corpus.

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), Naïve 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 tests were used to validate and compare the prediction capability of the models. Results show that 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.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/33aa930ebdd5f767b2095ae0d265de526668f89b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, neutral): “LMT model is one of the robust machine learning algorithms that its prediction capabilities is proven in some aspect such as determination of the flood prone areas (Khosravi et al. 2018).”
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, methodology): “These factors must be removed to increase the accuracy of the model (Khosravi et al., 2018).”
Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms (2021, background): “1, thenthere is a MC problem in a dataset [60].”
Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “Reference [184] proposed a hybrid forecasting technique called RSVRCPSO to accurately estimate the rainfall.”
A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers (2020, methodology): “96), there is significant differences between the performance of the models [72,85].”
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, background): “An AUC equal to 1 indicates that the model is perfect or ideal, whereas a value of 0 indicates an inaccurate model [3].”
Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees (2019, background): “Decision trees (DT), proposed by Quinlan [37], is a popular machine learning method that can be applied in solving many real world problems such as identifying risk factors for drug use [38], flash flood prediction [39], landslide spatial prediction [40], prediction of a short-term photovoltaic powe”