Water is one of the most important resources for human life and health. Global climate change, industrialization and urbanization pose serious dangers to existing water resources. Water quality has traditionally been predicted by expensive, time-consuming laboratory and statistical analysis. However, machine learning algorithms can be applied to determine the water quality index in real time efficiently and quickly. With this motivation, a dataset obtained from the Kaggle website was used to classify water quality in this research. Some features were found to be empty in the data set. Traditional methods (drop, mean imputation) and regression method were applied for null values. After the null values were completed, RF, Adaboost and XGBoost were applied for binary classification. Gridsearch and Randomsearch methods have been applied in hyper parameter optimization. Among all the algorithms used, the SXH hybrid method created with the Support Vector Regression (SVR) and XGBoost methods showed the best classification performance with 99.4% accuracy and F1-score. Comparison of our results with previous similar studies showed that our SVR XGboost Hybrid (SXH) model had the best performance ratio (Accuracy, F1-score). The performance of our proposed model is proof that hybrid machine learning methods can provide an innovative perspective on potable water quality.

Background: The agricultural sector accounts for more than 80% of global freshwater consumption, making accurate water demand modeling crucial for preserving these scarce resources, particularly in arid and semi-arid regions. Traditional water footprint (WF) analyses present significant challenges, as they require extensive datasets and complex modeling of soil-crop-water interactions. Machine learning (ML) methods offer advantages through their ability to process complex data relationships efficiently while maintaining high prediction accuracy. Previous ML applications in WF estimation have focused primarily on regional scales. This study extends the application to a global scale for wheat WF prediction, exploring the potential of ML in large-scale agricultural water management. Results: This study enhances WF modeling for wheat through the implementation of the AdaBoost algorithm, which offers reduced computation time, handles diverse geographical conditions effectively, and achieves high prediction accuracy with minimal calibration requirements. The model achieved a mean absolute error (MAE) of 108.5 m³/t, mean squared error (MSE) of 239.9 m³/t, and mean absolute percentage error (MAPE) of 1.51, along with a high prediction accuracy evidenced by a test score of 98.49% and an R ² value of 0.87. The study revealed distinct outcomes for different clustering methods, demonstrating the model's robustness across varying spatial scales. Conclusions: Our findings demonstrate that high-accuracy WF analysis can be achieved with fewer datasets and in a shorter time compared to traditional methods. The ML approach enhances both precision and efficiency of WF estimation for wheat cultivation, offering a practical tool for agricultural water management. This methodology provides valuable insights for researchers and policymakers working towards sustainable water resource management.

Accurate forecasting of multivariate time series is essential for energy management, grid optimisation, and policy planning. This study presents a hybrid deep learning and Transformer-based forecasting framework for predicting hourly electricity consumption across Turkey using nationwide data from Energy Exchange Istanbul (EPİAŞ) between 2018 and 2025. The dataset comprises 15 variables representing diverse energy sources and market indicators, including consumption, generation, and the market-clearing price (MCP). The proposed hybrid model integrates Long Short-Term Memory (LSTM), Bidirectional LSTM (BLSTM), and Gated Recurrent Unit (GRU) layers to capture both short- and long-term temporal dependencies, while a Transformer model leveraging multi-head self-attention mechanisms is used for comparison. All models were trained using standardised preprocessing, a 24 h lookback window, and optimised hyperparameters via GridSearchCV. Experimental results reveal that the hybrid model achieved the best overall performance, with MAE = 464.01, RMSE = 663.39, and R² = 0.9902, significantly outperforming the baseline and Transformer models. The Transformer demonstrated robust long-horizon learning capability (R² = 0.9257) but at a higher computational cost. These results confirm that combining multiple recurrent architectures enhances predictive accuracy and stability for large-scale, real-time energy forecasting. The proposed framework offers a reliable foundation for smart grid operations, demand prediction, and data-driven energy policy development.

Objectives: Determining low-density lipoprotein (LDL) is a costly and time-consuming operation, but triglyceride value above 400 (TG>400) always requires LDL measurement. Obtaining a fast LDL forecast by accurate prediction can be valuable to experts. However, if a high error margin exists, LDL prediction can be critical and unusable. Our objective is LDL value and level prediction with an error less than low total acceptable error rate (% TEa). Methods: Our present work used 6392 lab records to predict the patient LDL value using state-of-the-art Artificial Intelligence methods. The designed model, p-LDL-M, predicts LDL value and class with an overall average test score of 98.70 %, using custom, hyper-parameter-tuned Ensemble Machine Learning algorithm. Results: The results show that using our innovative p-LDL-M is advisable for subjects with critical TG>400. Analysis proved that our model is positively affected by the Hopkins and Friedewald equations normally used for (TG≤400). The conclusion follows that the test score performance of p-LDL-M using only (TG>400) is 7.72 % inferior to the same p-LDL-M, using Hopkins and Friedewald supported data. In addition, the test score performance of the NIH-Equ-2 for (TG>400) is much inferior to p-LDL-M prediction results. Conclusions: In conclusion, obtaining an accurate and fast LDL value and level forecast for people with (TG>400) using our innovative p-LDL-M is highly recommendable.