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Predictive capabilities of the proposed model were evaluated and it was observed that ANFIS performed superiorly for predicting daily flow discharge at the proposed site with spatially distributed rainfall as input. (2014) used ANFIS, ANN, multiple linear regression, and multiple nonlinear regression to forecast the peak flow of Khosrow Shirin catchment, positioned in the Fars region, Iran, on a daily basis. Based on the comparison of observed and estimated data, outcomes revealed that ANFIS performed better in predicting river flow discharge compared to customary models. Bisht & Jangid (2011) used ANFIS to develop river stage-discharge models at the Dhawalaishwaram barrage site in Andhra Pradesh, India. Obtained results demonstrated that ANFIS was effective and reliable to construct a flood forecasting model with better accuracy. (2006) proposed the construction of a flood forecast model using ANFIS in the Choshui River, Taiwan, and compared its performance with a back-propagation neural network (BPNN). In recent times, attention has been shifted from focusing on the applicability of ANN tools to importance on refining estimation capability and clarifying the inner conduct of ANN tools ( Maier & Dandy 2000 Sudheer & Jain 2004 Araghinejad 2013).Ĭhen et al. In the last decade, numerous developments have been made to improve both the enactment and consistency of ANN tools. 2007 Shu & Ouarda 2008) and modelling water quality ( Singh et al. 2012), forecast streamflow ( El-Shafie et al. Over past decades, ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models have been comprehensively utilized in a variety of engineering applications involving hydrology such as simulation of rainfall-runoff process ( Wu & Chau 2011), model groundwater problems ( Sahoo et al. Artificial neural networks (ANNs) act as suitable models for the problem mentioned above. Upstream circumstances intensely influence the flood flows in downstream zones therefore, a flood forecasting model needs to be developed which can detect accurately eloquent fundamental connection amid downstream and upstream situations. Evidence of this information is a complex investigation that has concerned researchers over past decades. Flood prediction and forecasting act as the essential practices to control flood events across the globe ( Young 2002 Campolo et al. Every year, substantial public and financial damages, as well as fatalities, are caused by dangerous storms worldwide, specifically in areas subjected to monsoon weather and regions with slow growth of water conservancy schemes ( Jiang et al. Results reveal that robust ANFIS-GOA outperforms standalone AI techniques and can make superior flood forecasting for all input scenarios.įorecasting various hydrological phenomena is of significant concern in the field of hydrology and is pivotal for appropriate water resources development and disaster management.
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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). 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. 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. Artificial Intelligence (AI) models have been effectively utilized as a tool for modelling numerous nonlinear relationships and is suitable to model complex hydrological systems. Flooding leads to severe civic and financial damage, particularly in large river basins, and mainly affects the downstream regions of a river bed.
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Accurateness in flood prediction is of utmost significance for mitigating catastrophes caused by flood events.