A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods

By Khabat Khosravi, Himan Shahabi, Binh Thai Pham, Jan Adamowski, Ataollah Shirzadi, Biswajeet Pradhan, Jie Dou, Hai-Bang Ly, Gyula Gróf, Huu Loc Ho and 3 more. Journal of Hydrology, 2019

629 citations recorded by the source corpus.

Abstract Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China’s most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/60b75170baddb4301415bb4f25359531588cf8e3

How researchers see this paper

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

In Environmental Engineering

1. Hoang Nguyen
2. Baharin bin Ahmad
3. Khabat Khosravi
4. Himan Shahabi
5. Hossein Moayedi

In Global and Planetary Change

1. Ataollah Shirzadi
2. Baharin bin Ahmad
3. Khabat Khosravi
4. Himan Shahabi
5. N. Al‐Ansari
6. Marten Geertsema
7. John J. Clague
8. Hoang Nguyen

In Water Science and Technology

1. Himan Shahabi
2. Assefa M. Melesse
3. Baharin bin Ahmad
4. Hossein Moayedi

Citations in the corpus

• A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, methodology): “Khosravi et al. [96] applied standalone ANFIS as well as ANFIS hybrid with genetic, imperialist competitive, and differential evolution algorithms for reference evaporation estimation.”
• 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, methodology): “In addition, a variety of multivariate methods have been used in flood hazard studies, notably logistic regression [19,20] and multicriteria decision-making (MCDM) methods such as analytic hierarchy process (AHP) [21–23] analytic network process (ANP) [24], vlse kriterijuska optamizacija I komoromis”