Abstract
Water is a vital yet increasingly endangered resource, that stands on the precipice of depletion and degradation, threatened by pollution, overexploitation, habitat alteration, and the looming spectre of climate change. Growing demand for water from various productive sectors and the escalating shifts in weather patterns are among the primary factors contributing to resource strains. Such factors lead to abrupt deterioration in terms of quantity and quality of water. Recently, technological intervention is playing a significant role in the mitigation of the stress on water resources leading to a better understanding of the dynamics behind consumption and replenishment. The substantial rise in the utilization of novel sensors is actively enhancing monitoring capabilities and facilitating data collection. There remains an ongoing requirement to enhance and advance methodologies for effectively analyzing the exponential surge in data. In this regard, the use of Artificial Intelligence (AI) towards supporting the management of water reserves can bring significant benefits in terms of the protection and sustainable utilization of the resource that is the basis of all life. In this paper, we propose an AI-based system for predicting Groundwater Level (GWL) focusing on Gorgovivo spring, located in province of Ancona, Italy as a case study. The predictive model was evaluated using the following criteria: Mean Average Error (MAE), Mean Squared Error (MSE) and correlation. Our case study concludes that the implementation of the prototype system produces valuable prediction results for GWL compared to other state-of-the-art approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In this work we selected as a case study the Gorgovivo spring, located in Serra San Quirico, province of Ancona, Italy.
References
Afrifa, S., Zhang, T., Appiahene, P., Varadarajan, V.: Mathematical and machine learning models for groundwater level changes: a systematic review and bibliographic analysis. Future Internet 14(9), 259 (2022)
Aguilera, H., Guardiola-Albert, C., Naranjo-Fernández, N., Kohfahl, C.: Towards flexible groundwater-level prediction for adaptive water management: using Facebook’s prophet forecasting approach. Hydrol. Sci. J. 64(12), 1504–1518 (2019)
Anshuman, A., Eldho, T.: Feeding static values to LSTMs for Seq2Seq learning for simultaneous source identification and parameter estimation in groundwater. In: AGU Fall Meeting Abstracts, vol. 2022, pp. H33B–05 (2022)
Chang, F.J., Guo, S.: Advances in hydrologic forecasts and water resources management. Water 12(6), 1819 (2020)
Dadhich, A.P., Goyal, R., Dadhich, P.N.: Assessment and prediction of groundwater using geospatial and ANN modeling. Water Resour. Manage 35, 2879–2893 (2021)
Galdelli, A., Mancini, A., Frontoni, E., Tassetti, A.N.: A feature encoding approach and a cloud computing architecture to map fishing activities. In: 17th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA) (2021)
Ho, S., Xie, M.: The use of ARIMA models for reliability forecasting and analysis. Comput. Ind. Eng. 35(1), 213–216 (1998)
Khan, J., Lee, E., Balobaid, A.S., Kim, K.: A comprehensive review of conventional, machine leaning, and deep learning models for groundwater level (GWL) forecasting. Appl. Sci. 13(4), 2743 (2023)
Khozani, Z.S., Banadkooki, F.B., Ehteram, M., Ahmed, A.N., El-Shafie, A.: Combining autoregressive integrated moving average with long short-term memory neural network and optimisation algorithms for predicting ground water level. J. Clean. Prod. 348, 131224 (2022)
Le, X.H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7), 1387 (2019)
Najafabadipour, A., Kamali, G., Nezamabadi-pour, H.: The innovative combination of time series analysis methods for the forecasting of groundwater fluctuations. Water Resour. 49(2), 283–291 (2022)
NumPy Developers: Numpy correlation. https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html. Accessed 12 May 2023
Ren, H., Cromwell, E., Kravitz, B., Chen, X.: Using long short-term memory models to fill data gaps in hydrological monitoring networks. Hydrol. Earth Syst. Sci. 26(7), 1727–1743 (2022)
Tao, H., et al.: Groundwater level prediction using machine learning models: a comprehensive review. Neurocomputing 489, 271–308 (2022)
Tassetti, A.N., Galdelli, A., Pulcinella, J., Mancini, A., Bolognini, L.: Addressing gaps in small-scale fisheries: a low-cost tracking system. Sensors 22(3), 839 (2022)
Taylor, S.J., Letham, B.: Forecasting at scale. PeerJ Preprints (2017)
Tazioli, A.: Does the recharge area of a Spring Vary from year to year? Information from the water isotopes. Ital. J. Eng. Geol. Environ 2017, 41–56 (2017)
Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., Rajagopal, R.: NeuralProphet: Explainable Forecasting at Scale (2021)
United Nations: Transforming Our World: The 2030 Agenda for Sustainable Development (2015). https://sdgs.un.org/2030agenda. Accessed 12 May 2023
World Health Organization and United Nations Children’s Fund: Progress on household drinking water, sanitation and hygiene 2000–2017: special focus on inequalities. UNESCO (2019)
World Water Assessment Programme: 2018 UN World Water Development Report, Nature-based Solutions for Water. UNESCO (2018)
Zaresefat, M., Derakhshani, R.: Revolutionizing groundwater management with hybrid AI models: a practical review. Water 15(9), 1750 (2023)
Zarinmehr, H., Tizro, A.T., Fryar, A.E., Pour, M.K., Fasihi, R.: Prediction of groundwater level variations based on gravity recovery and climate experiment (GRACE) satellite data and a time-series analysis: a case study in the Lake Urmia basin, Iran. Environ. Earth Sci. 81(6), 180 (2022)
Acknowledgements
This research was conducted in synergy with Viva Servizi S.p.A. We would like to thank the Civil Protection Department of Marche region and the Agency for Services in the Agro-Food Sector of the Marche Region (ASSAM), for sharing rainfall stations data. The authors would provide the data and code upon request to enable the scientific community to reproduce the experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Galdelli, A., Narang, G., Migliorelli, L., Izzo, A.D., Mancini, A., Zingaretti, P. (2023). An AI-Driven Prototype for Groundwater Level Prediction: Exploring the Gorgovivo Spring Case Study. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-43153-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43152-4
Online ISBN: 978-3-031-43153-1
eBook Packages: Computer ScienceComputer Science (R0)