Version 1
: Received: 1 June 2023 / Approved: 1 June 2023 / Online: 1 June 2023 (12:13:24 CEST)
How to cite:
Ghafoor, H.; Umer, R.; Muhammad, J.; Rauf, Z.; Ali, F.; Shahwani, M.N. Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models. Preprints2023, 2023060085. https://doi.org/10.20944/preprints202306.0085.v1
Ghafoor, H.; Umer, R.; Muhammad, J.; Rauf, Z.; Ali, F.; Shahwani, M.N. Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models. Preprints 2023, 2023060085. https://doi.org/10.20944/preprints202306.0085.v1
Ghafoor, H.; Umer, R.; Muhammad, J.; Rauf, Z.; Ali, F.; Shahwani, M.N. Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models. Preprints2023, 2023060085. https://doi.org/10.20944/preprints202306.0085.v1
APA Style
Ghafoor, H., Umer, R., Muhammad, J., Rauf, Z., Ali, F., & Shahwani, M.N. (2023). Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models. Preprints. https://doi.org/10.20944/preprints202306.0085.v1
Chicago/Turabian Style
Ghafoor, H., Fatima Ali and Muhammad Naeem Shahwani. 2023 "Predicting Groundwater Levels at Colorado State of USA Using ARIMA and ANN Models" Preprints. https://doi.org/10.20944/preprints202306.0085.v1
Abstract
Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
