Prediction of Water Consumption for New York city using Machine Learning

As we know that in every city the demand for water is increasing day by day, it is becoming a very huge challenge to upgrade the filtration plants to meet the requirements. To do this for a particular city, the first task is to know the upcoming water requirement of that city. Some traditional approaches require a great deal to manual work, and it is very challenging to assess the performance of these traditional approaches. This problem was resolved by several researchers who proposed various algorithms using the Machine Learning algorithms like the Traversal grey model, Least Square Support Vector Machine and Based on Process Neural Network. To predict the consumption of water with maximum performance, in our work we have conducted a comparative study of a range of machine learning algorithms with some attribute selection techniques, k-folds cross-validation, and pre-processing techniques to boost the accuracy of these models. The performance of each algorithm will be evaluated on two parameters using the same dataset which will provide a comparison for the effectiveness of individual algorithms. These two parameters are Mean Absolute Error and Average Difference of Estimated and Real Value. At the end after comparing the results we get to pick the best algorithm to solve this problem. The algorithms used are Least Square Support Vector Machine, Lasso, XGBoost, Ridge regression, and a Hybrid model proposed which we have proposed in this paper. The obtained results proves that the hybrid model is the most suitable for the prediction of water consumption.