Abstract
Multi-step-ahead forecasting of groundwater level (GWL) is an important object that can be utilized for long-term policies and effective implementation of mitigation measures in the future. In this study, first three single artificial intelligence (AI)-based models including feed-forward neural network (FFNN), adaptive neural fuzzy inference system (ANFIS), and the group method of data handling (GMDH) network were used for predicting the multi-step-ahead GWL of Ghorveh–Dehgolan plain (GDP). Then, as a post-processing step to enhance the outcomes of the single models, their results were combined via two linear (simple and weighted) and one nonlinear (neural network) ensemble techniques. The results showed the superiority of the neural averaging ensemble technique because of its ability to cope with complex data. The neural model ensemble could improve the accuracy of the single models up to 23% in the testing phase. It could be concluded that the model ensemble techniques could enhance the performance of the single models for reliable forecasting of the future GWL condition.
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
Similar content being viewed by others
References
Chen W, Panahi M, Khosravi K, Reza H, Rezaie F (2019) Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. J Hydrol 572:435–448. https://doi.org/10.1016/j.jhydrol.2019.03.013
Gong Y, Zhang Y, Lan S, Wang H (2016) A Comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee, Florida. Water Resour Manage 30:375–391. https://doi.org/10.1007/s11269-015-1167-8
Rajaee T, Ebrahimi H, Nourani V (2019) A review of the artificial intelligence methods in groundwater level modeling. J Hydrol 572:336–351. https://doi.org/10.1016/J.JHYDROL.2018.12.037
Tao H, Al-Khafaji ZS, Qi C, Zounemat-Kermani M, Kisi O, Tiyasha T, Chau K-W, Nourani V, Melesse AM, Elhakeem M, Farooque AA, Pouyan Nejadhashemi A, Khedher KM, Alawi OA, Deo RC, Shahid S, Singh VP, Yaseen ZM (2021) Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions. Eng Appl Comput Fluid Mech 15:1585–1612. https://doi.org/10.1080/19942060.2021.1984992
Lee S, Lee K-K, Yoon H (2019) Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors. Hydrogeol J 27:567–579. https://doi.org/10.1007/s10040-018-1866-3
Azad A, Karami H, Farzin S, Mousavi SF, Kisi O (2019) Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Sci Eng 12:45–54. https://doi.org/10.1016/J.WSE.2018.11.001
Lambert RSC, Lemke F, Kucherenko SS, Song S, Shah N (2016) Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling. Math Comput Simul 128:42–54. https://doi.org/10.1016/j.matcom.2016.04.005
Elkiran G, Nourani V, Abba SI (2019) Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. J Hydrol 577:123962. https://doi.org/10.1016/J.JHYDROL.2019.123962
Nourani V, Ghaneei P, Kantoush SA (2021) Robust clustering for assessing the spatiotemporal variability of groundwater quantity and quality. J Hydrol 604:127272. https://doi.org/10.1016/J.JHYDROL.2021.127272
Tsai TM, Yen PH (2017) GMDH algorithms applied to turbidity forecasting. Appl Water Sci 7–1151–1160. https://doi.org/10.1007/s13201-016-0458-4
Foroumandi E, Nourani V, Dabrowska D, Kantoush SA (2022) Linking spatial-temporal changes of vegetation cover with hydroclimatological variables in terrestrial environments with a focus on the Lake Urmia basin 11. https://doi.org/10.3390/land11010115
Sharghi E, Nourani V, Zhang Y, Ghaneei P (2022) Conjunction of cluster ensemble-model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains. J Hydrol 610:127984. https://doi.org/10.1016/j.jhydrol.2022.127984
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nourani, V., Ghaneei, P., Sharghi, E. (2022). Multi-Step-Ahead Forecasting of Groundwater Level Using Model Ensemble Technique. In: Kim, J.H., Deep, K., Geem, Z.W., Sadollah, A., Yadav, A. (eds) Proceedings of 7th International Conference on Harmony Search, Soft Computing and Applications. Lecture Notes on Data Engineering and Communications Technologies, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-19-2948-9_24
Download citation
DOI: https://doi.org/10.1007/978-981-19-2948-9_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2947-2
Online ISBN: 978-981-19-2948-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)