The long short-term memory model as the neural networks approach in modeling water supply structural production

Abstract

Almost all cities in Indonesia are moving to find alternatives for providing clean water as the population increases, so the demand for clean water volume is also increasing. Water wastage issues arise as water production continues to rise, highlighting the need for accurate predictions of future water production levels. Modeling clean water supply structural movements as an effort to predict water needs in a city in the future is important. This can be done by considering water usage data over time and factors that trigger clean water demand. This research proposes a long short term memory (LSTM) model that adopts a neuro informatics model as the neural networks approach for modeling water supply. The architecture of the LSTM used in this research employs one hidden layer with 32 neurons. The findings demonstrate that LSTM model can predict water production levels accurately with mean absolute percentage error (MAPE) less than 5% both for training and testing data set. These results categorize the LSTM model as a reliable forecasting tool for water production levels. Therefore, modeling using the LSTM method is a preferable choice for predicting water production aiding relevant parties in planning clean water resources tailored to the needs of population.