Reservoir Inflow Prediction using Multi-model Ensemble System

Regardless of multiple reservoirs built across the rivers to control the flow of water bodies, yet many calamities in low lying areas have occurred in recent times. One of the reasons for these is the legacy techniques being in dams for flow management. Since Machine Learning algorithms are making good progress in accurately predicting future probabilities based on past data by using the statistical methods as its basis these techniques can be applied to train the machine model on weather reports and Dam flow control and capacity data so as to provide efficient control over Dam water level management and create better alert systems in case of calamities. This paper presents an evaluation of a few machine learning algorithms like LOWESS, Logistic Regression, and deep learning techniques based on Recurrent neural networks to predict Reservoir Inflow. Also, it makes use of the ensembling/ bagging technique on the results of the aforementioned algorithms to improve the accuracy of the model.