Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India.

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Advanced machine learning-based kharif maize evapotranspiration estimation in semi-arid climate.

Accurate Crop Evapotranspiration (ETc) estimation is crucial for understanding hydrological and agrometeorological processes, yet it's challenged by multiple parameters, data variations, and lack of continuity. These limitations restrict numerical methods application. To address this, the study aims to develop and assess ML models for daily maize ETc in semi-arid areas, utilizing varied weather inputs. Five ML models viz., Category Boosting (CB), Linear Regression (LR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were developed and validated for the ICAR-IARI, New Delhi, Research Station. Penman-Monteith (PM) model estimated ETc values are used as the standard for comparing the performance of the ML model values. Results revealed that the SVM model achieved the highest coefficient of determination (R2) among all models, with a value of 0.987. Furthermore, the SVM model exhibited the lowest model errors (MAE = 0.121 mm day-1, RMSE = 0.172 mm day-1, and MAPE = 4.37%) compared to other models. The ANN model also demonstrated promising results, comparable to the SVM model. Notably, the wind speed parameter was found most influential input parameter. In conclusion, SVM or ANN could be considered reliable alternative methods for the accurate estimation of kharif maize crop ETc in the semi-arid climate.