Forecasting Water Quality Index of the Ganga River Using CCL Hybrid Deep Neural Network
Keywords:
Ensemble Learning, Ganga River, Water Quality (WQ), Water Quality Index (WQI), Deep Learning ModelsAbstract
In this paper, Convolutional Neural Network-Convolutional Neural Network- Long Short-Term Memory (CNN-CNN_LSTM) hybrid deep learning neural network is developed to forecast the water quality of the river Ganga. Various deep learning models like LSTM, CNN, CNN_LSTM have been designed as baseline models to compare the outcome to the proposed model. Water Quality parameters data collected from ten base stations stablished by Uttarakhand Pollution Control Board is used for training & testing of the model developed. Water Quality Index is calculated using basic four Water Quality Parameters like BOD (Biochemical Oxygen Demand), pH (potential of Hydrogen), DO (Dissolved Oxygen), Temperature. The proposed CNN-CNN_LSTM(CCL) model provides better forecasting results for Water Quality Index (WQI).
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