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.

Traditionally, ASR expansion is determined using experimental and finite element method (FEM) based numerical modelling. However, these methods are time-consuming and computationally costly, which makes ASR prediction challenging. Machine learning (ML) techniques can serve as effective alternatives for the early detection of expansion in concrete structures. In this study, we developed four different machine learning models – extreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), and k-nearest neighbor (KNN) to predict the ASR expansion in concrete using a comprehensive dataset with 1896 data points. Each model was evaluated based on the model performance and XGBoost shows the most effective model for predicting the ASR expansion with R² of 0.99 for training and R² of 0.98 for testing. After identifying the best-performing model, Shapley Additive Explanations (SHAP) were employed to interpret its predictions. This approach provides insights into the model’s decision-making process, clarifying the complex nature of machine learning algorithms. Using Shapley Additive Explanations (SHAP), time and the average size of reactive aggregate were identified as critical parameters influencing ASR expansion. This research holds significant value for the construction industry, as accurately predicting ASR expansion can lead to optimized material usage and improved structural performance.

Airports are dynamic but pose complex environmental concerns because of their air quality. The air quality in the vicinity of the airports can deteriorate due to heavy vehicular traffic and aircraft handling. The air quality interacts with meteorological conditions; thus, understanding these links would benefit any mitigation activities in the search for sustainable solutions. However, these interactions are complex and nonlinear. Therefore, interpretable machine learning techniques can effectively be used to understand complex environmental scenarios. This study examines atmospheric temperature prediction at Dublin Airport, Ireland, using combined air quality and meteorological observations. Hourly data resulting in 3319 observations were used in training, testing, and validating the developed machine learning prediction models. Among the tested machine learning algorithms, namely K-Nearest Neighbours (KNN), Support Vector Regression (SVR), Random Forest (RF), Multi-Layer Perceptron (MLP), and Decision Tree (DT), KNN was selected as the best model based on its validation performance, achieving R² = 0.85, RMSE = 1.73 °C and MAE = 1.19 °C in the testing stage. This accuracy mainly reflects the estimation of temperature within the observed period. Shapley Additive Explanations (SHAP) revealed the black-box nature and found that CO, SO₂, O₃, and wind direction were the most influential variables contributing to atmospheric temperature predictions. The findings can then be used as a foundation for any future studies relating to aviation-related emissions, greenhouse gases, air quality, and atmospheric temperature. This study demonstrates the potential for developing similar prediction models for other busy airports and then incorporating necessary policy decisions to mitigate any adverse effects.