Groundwater Level Modeling Using Multiobjective Optimization with Hybrid Artificial Intelligence Methods
Author:
Funder
The study was supported by Maa- ja vesitekniikan tuki ry foundation
Publisher
Springer Science and Business Media LLC
Subject
General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s10666-023-09938-6.pdf
Reference98 articles.
1. Adnan, R. M., Dai, H.-L., Mostafa, R. R., Islam, A. R. M. T., Kisi, O., Heddam, S., & Zounemat-Kermani, M. (2022). Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data. Geocarto International, 38(1). https://doi.org/10.1080/10106049.2022.2158951
2. Agoubi, B., & Kharroubi, A. (2019). Groundwater depth monitoring and short-term prediction: Applied to El Hamma aquifer system, southeastern Tunisia. Arabian Journal of Geosciences, 12, 1–10.
3. Li, F., Zhao, Y., Feng, P., Zhang, W., & Qiao, J. (2015). Risk assessment of groundwater and its application. Part I: Risk grading based on the functional zoning of groundwater. Water Resources Management, 29, 2697–2714.
4. Noori, R., Maghrebi, M., Mirchi, A., Tang, Q., Bhattarai, R., Sadegh, M., Noury, M., Torabi Haghighi, A., Kløve, B., & Madani, K. (2021). Anthropogenic depletion of Iran’s aquifers. Proceedings of the National Academy of Sciences, 118(25), e2024221118.
5. Torabi Haghighi, A., Abou Zaki, N., Rossi, P. M., Noori, R., Hekmatzadeh, A. A., Saremi, H., & Kløve, B. (2020). Unsustainability syndrome—From meteorological to agricultural drought in arid and semi-arid regions. Water, 12(3), 838.
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