The lack of water is one of the most crucial problems of our day; therefore, optimized water resource management and predictions gathered by patrons are of utmost importance. In the turmoil of a country like India, which lives a variety of lifestyles and has a complicated network of rivers, the urgent need for an active point of view to take care of water shortages becomes exceptionally vital. In this study, India’s groundwater, available at the district level for the year 2017, was the area of focus, with this analysis utilizing a dataset of 689 rows, each representing a district, and 16 columns describing the various groundwater extraction and recharge metrics. The study involves five regression models adapting RandomForestRegressor, DecisionTreeRegressor, MLPRegressor, KNeighborsRegressor, and SupportVectorRegression for water resource evaluation and prediction. Every model is appraised by using a thorough metrics set where we incorporate Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Explained Variance Score (EVS), Max Error, Median Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R-squared (R2), among others. Our results put the spotlight on RandomForestRegressor, making MSE measures the same as 0.000206624, endorsing its better performance versus the criteria considered. The approach used in this model provides us with an ensemble effect that makes it more robust in the sense that we can capture the interrelationships within the dataset in a comprehensive way. DecisionTreeRegressor also provides nice options for precision and transparency. The use of such models depicts the potential value of predictive analytics, which has the role of improving resource management and planning because we can all agree that the impending water crisis is also a fact. These research outcomes provide us with important data for well-informed decisionmaking and strategic management of water reserves through all avenues and most affected areas to air most of the impact of water scarcity.

In improving PV system performance, the parameters associated with electrical photovoltaic equivalent models play a pivotal role. However, due to the increased mathematical complexities and non-linear traits of PV cells, the precise prediction of these parameters is a challenging task. To estimate the parameters associated with PV models, a reliable, robust, and accurate optimization technique is needed. This paper introduces a new algorithm, Rat Swarm Optimizer (RSO), for obtaining the optimum PV cell and module parameters. The proposed method maintains an adequate balance between the exploration and exploitation phases to overcome premature particle issues. The results obtained using RSO are compared with those of other algorithms, i.e., Particle Swarm Optimization (PSO), Ant Lion Optimizer (ALO), Salp Swarm Algorithm (SSA), Harris Hawks Optimization (HHO), and Grasshopper Optimization (GOA), in this work. The modified one-diode model (MODM) and modified two-diode model (MTDM) are used to analyze the parameters of the mono-crystalline PV cell using the suggested RSO. The obtained findings imply that the parameters estimated by the suggested RSO are more accurate than those calculated by the other algorithms taken into consideration in the paper. The statistical results are compared, and it is clear that RSO is a very accurate, fast, and dependable approach for the parameter estimation of PV cells.

Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10⁻⁹ and for the Avista SR-12 model it was 5.65 × 10⁻⁸. To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.