PreprintArticleVersion 1This version is not peer-reviewed
Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models
Version 1
: Received: 1 October 2024 / Approved: 2 October 2024 / Online: 2 October 2024 (13:01:09 CEST)
How to cite:
Hoque, M. A.; Apon, A. A.; Hassan, M. A.; Adhikary, S. K.; Islam, M. A. Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models. Preprints2024, 2024100144. https://doi.org/10.20944/preprints202410.0144.v1
Hoque, M. A.; Apon, A. A.; Hassan, M. A.; Adhikary, S. K.; Islam, M. A. Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models. Preprints 2024, 2024100144. https://doi.org/10.20944/preprints202410.0144.v1
Hoque, M. A.; Apon, A. A.; Hassan, M. A.; Adhikary, S. K.; Islam, M. A. Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models. Preprints2024, 2024100144. https://doi.org/10.20944/preprints202410.0144.v1
APA Style
Hoque, M. A., Apon, A. A., Hassan, M. A., Adhikary, S. K., & Islam, M. A. (2024). Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models. Preprints. https://doi.org/10.20944/preprints202410.0144.v1
Chicago/Turabian Style
Hoque, M. A., Sajal Kumar Adhikary and Md Ariful Islam. 2024 "Enhanced Forecasting of Groundwater Level Incorporating Exogenous Variable: Evaluating Conventional Multivariate Time Series and Artificial Neural Network Models" Preprints. https://doi.org/10.20944/preprints202410.0144.v1
Abstract
Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels. As a part of the sustainable planning and effective management of water resources, it is crucial to assess the existing as well as future groundwater level (GWL) condition. In the current study, an attempt was made to model and forecast GWL using artificial neural networks (ANN) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA incorporating exogenous variables (ARIMAX) were adopted as the time series models. Kushtia district in Bangladesh was selected as the case study area, and GWL data of five monitoring wells in the study are used to demonstrate the modeling exercise. Rainfall was taken as the exogeneous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. The performance of each time series and ANN model was assessed based on various model evaluation criteria. It was evident from the results that the multivariate ARIMAX model (SSE of 15.361) performed better than the univariate ARIMA model with an SSE of 17.217 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the time series and ANN models, it was found that the ANN-based model outperformed the time series models with the enhanced forecasting accuracy (SSE of 9.894). Results also exhibit a significant correlation coefficient value R of 0.995 (ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Thus, the ANN approach was not only carried out for model building and simulation but also to provide a valuable tool for managing water resources amidst changing environmental conditions.
Keywords
groundwater level; exogenous variable; ANN; multivariate time series; ARIMAX
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
