Groundwater Level Forecasting in the Jakarta Groundwater Basin

Abstract

Groundwater is an important source of fresh water worldwide. Jakarta, the capital city of the Republic of Indonesia, is a large city with a population of more than 10 million and the center of the country’s economy and urban development. However, Jakarta has many challenges related to groundwater management. Changes in well GWL serve as a direct indicator of the effects of groundwater evolution, and GWL time series usually contain important information about aquifer dynamics. Therefore, water managers and engineers must model and predict GWL, identify and measure groundwater resources, and maintain a balance between supply and demand. Measuring and analyzing the groundwater table (GWL) of an aquifer are an important and valuable activity in the management of groundwater resources, and information on GWL variability can be used to determine groundwater availability. Accurate and reliable groundwater level estimation is very important because it provides important information about the quantitative groundwater status of an aquifer. The main advantage of AI models is their ability to reproduce nonlinear and complex processes without a full understanding of the underlying physics. As a result, the use of AI techniques in GWL modeling continues to grow, attracting the attention of scientists around the world. Using a nonlinear autoregressive network with extrinsic input (NARX) with a timestep of 14 days, this study is aimed at predicting the volatility of Jakarta’s GWL. Based on the research results, both JKT-01 and JKT-03 show a linear downward trend. On the other hand, JKT-02 and JKT-04 show a stable linear trend. GWL increases the linear trend of JKT-05. Variable results are generated by model performance. Model performance ( R ) varied between 0.2 and 0.9 during the training phase and between 0.2 and 0.9 during the validation phase. The overall performance of the model varies from 0.3 to 0.9. The diverse lithology and high pumping capacity in Jakarta are the reasons for the different modeling results. Forecasts for 14 days (14 timesteps) show that GWL remains constant at certain well locations, while GWL decreases at other locations. This is a consideration that stakeholders can consider to reduce the small effect of the daily GWL pattern as a result of NARX modeling.