Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R², $${R}_{adj}^2$$R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R² and $${R}_{adj}^2$$R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R² and $${R}_{adj}^2$$R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R² and $${R}_{adj}^2$$R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the testing phase of Coefficient of Determination (R = 0.9005), Mean Absolute Percentage Error (MAPE = 8.40%), and Relative Root Mean Square Error (rRMSE = 12.56%). Nevertheless, the ANFIS model outperformed the GA-ANN model in forecasting daily GSR, with the best values of indicators when testing the model being R = 0.9374, MAPE = 7.78%, and rRMSE = 10.54%. Generally, we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved, and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory. The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

This study investigates the impacts of urbanization in Başakşehir, Istanbul, through the analysis of critical environmental indicators: Land Surface Temperature, Normalized Difference Vegetation Index, Proportion of Vegetation, Normalized Urban Heat Island and Urban Thermal Field Variance Index. Using a hybrid supervised machine learning approach integrating Convolutional Neural Networks and Random Forest for Land Use/Land Cover classification, the research achieved an accuracy rate of 93.33%. The findings highlight the complex relationships among urban expansion, ecological health, and environmental changes, advocating sustainable urban planning strategies to address the challenges posed by rapid urbanization. Nonparametric tests, particularly the Mann–Kendall trend test and Sen’s slope estimator, assessed temporal trends in meteorological data, and statistically significant results were obtained for maximum and minimum temperatures (p < 0.001). These results highlight urbanization as a major driver of local climate change, including the Urban Heat Island (UHI) effect. The analysis also reveals vegetation degradation and recovery trends, highlighting the need for urban planning that includes green areas to reduce the UHI effect and enhance ecological resilience. This research provides valuable insights for policymakers by advocating effective conservation strategies that balance urban development with environmental sustainability.

The maritime industry faces increasing pressure to enhance vessel efficiency and reduce environmental impact, largely through structural weight reduction. This study presents the design and optimization of a lightweight Enclosed Mast for naval ships, using both steel and composite materials. The aim was to improve aerodynamic efficiency and reduce stress concentrations. Finite element analysis (FEA) and computational fluid dynamics (CFD) were employed to evaluate structural and aerodynamic performance. An artificial neural network (ANN), coupled with a genetic algorithm, identified optimal designs, while Monte Carlo simulations addressed uncertainties, and sensitivity analysis determined the most influential input parameters. The optimized mast achieved a 50% weight reduction and a 36% decrease in body thickness, along with improved stress distribution. Aerodynamically, it reduced low‐velocity zones and drag by stabilizing the wake and smoothing flow. The ANN model achieved over 97% accuracy and a low error of 0.05, validating the approach for future marine structure optimization.