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Accurately estimating evaporation is essential for water managers to formulate effective rules and policies. The complexity arising from intricate interactions within the soil-atmosphere system makes evaporation a challenging parameter to predict. In the past decade, Machine Learning techniques rooted in soft computing have emerged as potent tools for addressing the intricacies and non-linearities in hydrology. Surprisingly, there has been no prior research exploring the impact of reservoir water temperature on evaporation modeling. Consequently, this study aimed to employ a hybridized Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Salp Swarm Algorithm (SSA). The focus was on modeling the monthly evaporation of the Boukourdane Dam in Algeria for the period of September 1996 to August 2016 and examining how the reservoir's water temperature influences model performance based on four performance indicators, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), scatter index (SI) and Correlation Coefficient (R). The findings underscored the significance of incorporating the water temperature parameter. Among the models, ANFIS-HHO demonstrated the highest accuracy with MAE, RMSE and R values of 1.28, 1.21 mm and 0.92, respectively during test period. Moreover, results revealing a notable impact of reservoir water temperature on evaporation forecasting and the addition of this parameter provide an increase in R and decrease in RMSE about 4.54% and 17.98% respectively. [ABSTRACT FROM AUTHOR]

• An autonomous water pumping system (WPS) is suggested. • A hybrid energy system is optimized through seagull optimization algorithm (SOA). • The impacts of load management (LM) strategy in system sizing is analyzed. • Techno-economic evaluation in the presence and absence of load management is made. This study introduces an innovative irrigation system tailored for a tomato field in Algeria, optimizing its elements using Seagull Optimization Algorithm (SOA) over a 20-year lifespan. The system integrates photovoltaic (PV), wind turbine (WT), diesel generator (DG), battery, and tank storage for water pumping. The novelty lies in the incorporation of Load Management (LM), creating a dynamic water demand profile across four tomato development stages. Without LM, the optimal configuration yields a Net Total Present Cost (NTPC) of $14,140, corresponding to a Cost of Energy (COE) of 0.292 $/kWh. This configuration ensures a 24% Loss of Power Supply Probability (LPSP), a 41.2 % Loss of Water Supply Probability (LWSP), and an emission factor of 0.169 kg/kWh during 279 h of DG operation annually. With LM, the COE drops to $0.298/kWh, reducing the NTPC to $12,080. This LM strategy gives an 18 % LPSP, 20 % LWSP, and an Energy Fraction (EF) of 0.140 kg/kWh during 168 h of DG operation annually. The present application is a promising and cost-effective solution for optimizing Water Pumping Systems (WPS) in agricultural settings. [ABSTRACT FROM AUTHOR]

This paper is intended to solve the optimal power flow (OPF) dispatch in the presence of wind power generation (WPG) in the Adrar power system. Towards this aim, the performances of three powerful meta-heuristic algorithms-namely, the cuckoo search algorithm (CSA), firefly algorithm (FFA), and flower pollination algorithm (FPA) are investigated. The proposed algorithms are applied to best capture the active power produced with the minimum value of a multi-objective function. This latter includes: the fuel cost, the NOx emissions, and the imbalance cost of the WPGs. Furthermore, considering the uncertainties governing wind resources, the maximum wind power output is estimated using the wind speed carrying maximum energy. It was found that all algorithms perform well in providing accurate solutions. Interestingly, the convergence is reached in the first 135 iterations. A remarkable outcome of the present work is that CSA outperforms FPA and FFA. CSA has proved itself to be a great tool to optimize Adrar's power flow system in term of iterations and computational time. [ABSTRACT FROM AUTHOR]

Summary: In this article, an optimal design model using metaheuristic techniques to overcome such difficult optimization problems has been proposed. For this purpose, various well‐known metaheuristic algorithms have been used, namely: ant lion optimizer (ALO), bat algorithm (BA), cuckoo search algorithm (CSA), and gray wolf optimization algorithm (GWO). To provide optimal system configurations, an objective function based on the total net present cost (TNPC) has been specified while taking into account a number of constraints, including the loss of power supply probability (LPSP). The effectiveness of the used algorithms in solving such problem is assessed and their performances compared with each other. To assess the suggested model's effectiveness, a case study has been conducted to investigate a stand‐alone photovoltaic water pumping system (PVWPS), designed to supply water to a one‐hectare field of date palm trees. This field of an oasis located in the Ghardaïa region of Algeria consists of 130 date palm trees spaced 9 m apart. The simulation results show the effectiveness of the GWO algorithm over the other algorithms. GWO converges toward the optimal solutions for different heads and all reliability levels required by the customer. Another aspect, supported by the developed methodology, is the accounting of the excess water volume. It has been found that the relative surplus is inversely proportional to the PVWPS reliability level. For a head of 30 m, the relative excess is estimated at 120% for LPS0%, while it is 27% for LPSP of 10%. The same observations are made for the other heads. [ABSTRACT FROM AUTHOR]