Enhancing Water Quality Forecasting Reliability Through Optimal Parameterization of Neuro-Fuzzy Models via Tunicate Swarm Optimization

• Published in: International journal of advanced computer science & applications Vol. 15; no. 3
• Main Authors: Kumar, Kambala Vijaya, Kumar, Y Dileep, Godla, Sanjiv Rao, Ansari, Mohammed Saleh Al, El-Ebiary, Yousef A.Baker, Muniyandy, Elangovan
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2024
• Subjects: Accuracy; Algorithms; Artificial neural networks; Decision making; Environmental management; Forecasting; Fuzzy logic; Marine environment; Mathematical models; Optimization; Optimization techniques; Parameterization; Process parameters; Resource allocation; Swarm intelligence; Water quality
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.01503110

Forecasting water quality is critical to environmental management because it facilitates quick decision-making and resource allocation. On the opposite hand, current methods are not always able to produce reliable forecasts, which is often due to challenges in parameter optimization for complex models. This research presents a novel approach to enhance the forecasting accuracy of water quality by optimizing neuro-fuzzy models using Tunicate Swarm Optimisation (TSO). The introduction highlights the limitations of current techniques as well as the necessity for precise estimates of water quality. One of the drawbacks is that neuro-fuzzy models are not well-modelled, which makes it harder for them to identify the minute patterns in data on water quality. The suggested approach is unique in that it applies TSO, an optimization algorithm inspired by nature that emulates tunicates' behaviour, to the neuro-fuzzy models' parameter optimization process. The highly complex parameter space is effectively navigated by TSO's swarm intelligence, which strikes a balance between exploration and exploitation to improve model performance. To optimize model parameters, the process comprises three steps: creating an objective function, defining the neuro-fuzzy model, and seamlessly integrating TSO. By mimicking the motions of tunicates as they look for the best conditions in the marine environment, TSO constantly optimizes the variables. Experiments demonstrate that the proposed strategy is more effective than traditional optimization techniques in forecasting water quality. As seen by the optimised neuro-fuzzy model's increased prediction accuracy and several dataset validations, Tunicate Swarm Optimisation has potential for reliable environmental forecasting. This work presents a potential path for improved environmental decision-making systems by offering an optimisation strategy inspired by nature that overcomes the limitations of existing methods and enhances water quality forecasting tools.