Groundnut, being a widely consumed oily seed with significant health benefits and appealing sensory profiles, is extensively cultivated in tropical regions worldwide. However, the yield is substantially impacted by the changing climate. Therefore, predicting stressed groundnut yield based on climatic factors is desirable. This research focuses on predicting groundnut yield based on several combinations of climatic factors using artificial neural networks and three training algorithms. The Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient algorithms were evaluated for their performance using climatic factors such as minimum temperature, maximum temperature, and rainfall in different regions of Sri Lanka, considering the seasonal variations in groundnut yield. A three-layer neural network was employed, comprising a hidden layer. The hidden layer consisted of 10 neurons, and the log sigmoid functions were used as the activation function. The performance of these configurations was evaluated based on the mean squared error and Pearson correlation. Notable improvements were observed when using the Levenberg–Marquardt algorithm as the training algorithm and applying the natural logarithm transformation to the yield values. These improvements were evident through the higher Pearson correlation values for training (0.84), validation (1.00) and testing (1.00), and a lower mean squared error (2.2859 × 10⁻²¹) value. Due to the limited data, K-Fold cross-validation was utilized for optimization, with a K value of 5 utilized for the process. The application of the natural logarithm transformation to the yield values resulted in a lower mean squared error (0.3724) value. The results revealed that the Levenberg–Marquardt training algorithm performs better in capturing the relationships between the climatic factors and groundnut yield. This research provides valuable insights into the utilization of climatic factors for predicting groundnut yield, highlighting the effectiveness of the training algorithms and emphasizing the importance of carefully selecting and expanding the climatic factors in the modeling equation.

Accurate ambient temperature prediction is essential for climate monitoring, urban planning, and environmental management, particularly in regions experiencing rapid climatic variability such as Sri Lanka. This study investigates the application of explainable machine learning models for short-term ambient temperature prediction in Battaramulla, Sri Lanka. Five regression algorithms-K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Histogram-based Gradient Boosting Regressor (HGBR) were evaluated using 14 meteorological and environmental predictors, including temporal variables, relative humidity, solar radiation, rainfall, wind speed, and air pollutant concentrations (CO2, NOₓ, CH4, O3, CO, PM2.5, and PM10). Among the models tested, HGBR demonstrated superior predictive performance, achieving R² values of 1.00 (training) and 0.96 (testing), with corresponding mean squared error values of 0.01 and 0.11. Model interpretability was examined using SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) analyses, and Accumulated Local Effects (ALE), which identified several features as the most influential predictors. Model validation using 192 real-time observations showed close agreement between predicted and measured temperatures, although the evaluation was limited to a single location and time period. A web-based application, ‘Therma’, was developed to facilitate practical deployment of the model for localized temperature estimation. Overall, this study demonstrates the utility of explainable machine learning for localized climate prediction while highlighting the need for broader spatiotemporal validation in future work.