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|Water Demand Forecasting|
Diksha and Lokendra singh Songare
Whenever an engineer given a task of designing a treatment unit for water, the first step will be what the water demand is by total number of population? Now it’s very difficult job to find that exact quantity of water demand so exact quantity cannot be estimated because its changes from time to time, seasonal variations are there, early variations are there, and different types of variations are there. But still there are certain formulas, techniques such machine learning and deep learning and the models such as Auto Regressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average with Exogenous Factors (SARIMAX), and Long Short Term Memory (LSTM) model through which these kind of forecasting can be done.
Key Words: Demand Forecasting, Exploratory Data Analysis, Statistical Modelling, Auto Regressive Integrated Moving Average, Recurrent Neural Network and Long Short Term Memory (LSTM).
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