Efficacy of ANFIS-GOA technique in flood prediction: a case study of Mahanadi river basin in India

Abstract

Accurateness in flood prediction is of utmost significance for mitigating catastrophes caused by flood events. Flooding leads to severe civic and financial damage, particularly in large river basins, and mainly affects the downstream regions of a river bed. Artificial Intelligence (AI) models have been effectively utilized as a tool for modeling numerous nonlinear relationships and is suitable to model complex hydrological systems. Therefore, the main purpose of this research is to propose an effective hybrid system by integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) model with meta-heuristic Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) for flood prediction in River Mahanadi, India. Robustness of proposed meta-heurestics are assessed by comparing with a conventional ANFIS model focusing on various input combinations considering 50 years of monthly historical flood discharge data. The potential of the AI models is evaluated and compared with observed data in both training and validation sets based on three statistical performance evaluation factors, namely root mean squared error (RMSE), mean squared error (MSE) and Wilmott Index (WI). Results reveal that robust ANFIS-GOA outperforms standalone AI techniques and can make superior flood forecasting for all input scenarios.