Water quality plays a vital role in public health, environmental sustainability and ecosystem balance. However, industrialization, urbanization, and agricultural activities cause water pollution to increase, threatening both human health and aquatic ecosystems. Traditional water quality monitoring methods are usually time-consuming, costly, and require manual intervention. Therefore, developing automatic, data-driven, and high-accuracy prediction models is crucial for sustainable water management. This study created a hybrid ConvLSTM model to increase prediction accuracy. The created model was comparatively analyzed with RF, SVR, XGBoost, MLP, CNN, and LSTM. The dataset used includes historical measurement values of chemical pollutants. Input data includes geographical coordinates of sample points, chemical parameter type, and compliance with regulatory standards. Experimental results show that ConvLSTM provides the lowest prediction errors by learning spatial and temporal dependencies and reaches the highest accuracy rate with 0.994 R2 compared to other models.
Utku et al. (Sun,) studied this question.