HydroNet-Monitoring Water Health: A Comprehensive Approach to Evaluation and Forecasting Quality

Among the main motivations of making forecasts on water quality, related needs are identified in public health management and environment. A novel hybrid approach can be described as combining ensemble approaches with deep learning to improve the prediction capability. In fact, it is the integration of Random Forest ensemble with Convolutional Long Short-Term Memory network, which subsequently allows spatial and temporal trends within water-quality data to be incorporated. In this, the model was trained over a large dataset of water samples originating from various sources, with considerable water quality indicators. Our hybrid methodology shows higher predication accuracy along with robustness compared to the traditional classic techniques associated with approaches of machine learning. The ConvLSTM part of the model is efficient for handling complex temporal dependencies and overfitting of the random forest ensemble improves the generalization and interpretability of the model. Observably, from the feature importance analysis, pH and dissolved oxygen are the most critical measurements associated with water quality. The architecture proposed covers real-time data collection, preprocessing, two-stage pipeline of the prediction with data augmentation besides the hyperparameter optimization techniques. The results of the above architecture show appreciable improvements in both computational efficiency and the predictions' accuracy. In the process, the contribution advances a discipline that provides more flexible and reliable frameworks for predicting water quality, likely to find application in early warning systems and well-informed policy-making for the sustainable management of water resources.