Water Demand Modeling using Machine Learning Method in Bandung City, Indonesia

Abstract

This research was conducted at Bandung City with the aim of building a model using machine learning methods so that it can estimated clean water demands in Bandung City, as well as knowing the external factors that are considered to affect the model. Machine learning is a part of Artificial Intelligence (AI) discipline. The modeling is carried out using independent variables in the form of climate parameters which are rainfall, rainy days, and humidity, as well as the dependent variable in the form of drinking water needs which are represented by raw water. Data collection is done through secondary data. The model was built by using the TPOT module, and produces the AdaBoost.R2 algorithm as the most optimal model, by using the model algorithm, the best sub-model is produced with the most influential external factors, namely rainy days and humidity which has an MAE of 326,077.70 and a MAPE of 4.75%. This model is compared with the ARIMA model which has an MAE of 330,672.088 and an MAPE of 5.07%.