Abdi, E., Ali, M., Guimarães Santos, C. A., Olusola, A., & Ghorbani, M. A. (2024). Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models.
Groundwater for Sustainable Development, 26,101213.
https://doi.org/10.1016/j.gsd.2024.101213
Alfarrah, N., & Walraevens, K. (2018). Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions.
Water, 10(2), 143.
https://doi.org/10.3390/w10020143
Afzaal, H., Farooque, A.A., Abbas, F., Acharya, B., & Esau, T. (2020). Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning
. Water, 12 (1). 35- 49.
https://doi.org/10.3390 /w12010005
Alizamir, M., Kisi, O., & Zounemat-Kermani, M. (2018). Modelling long-term groundwater fluctuations by extreme learning machine using hydroclimatic data.
Hydrology Science Journal. 63 (1), 63–73.
https://doi.org/10.1080/02626667.2017.1410891
Bowes, B.D., Sadler, J.M., Morsy, M.M., Behl, M., & Goodall, J.L. (2019). Forecasting groundwater table in a flood prone coastal city with long short-term memory and recurrent neural networks.
Water, 11 (5). 1098.
https://doi.org/10.3390/w11051098
Barzegar, R., Fijani, E., Moghaddam, A.A., & Tziritis, E. (2017). Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.
Science of the Total Environment, 599, 20–31.
https://doi.org/10.1016/j.scitotenv.2017.04.189
Chen, T., & Guestrin, C. (2016).
XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
https://doi.org/10.1145/2939672.2939785
Chen, Y.,
Chen, W.,
Chandra P. S.,
Saha, A.,
Chowdhuri, I.,
Adeli, B.,
Janizadeh, S.,
Dineva, A. A.,
Wang, X., &
Mosavi, A. (2021). Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International journal, 37(19), 5564–5584.
https://doi.org/10.1080/10106049.2021.1920635
Franses, P. H., & Van Dijk, D. (2000).
Non-linear time series models in empirical finance. Cambridge, United Kingdom: Cambridge University Press.
https://doi.org/10.1017/CBO9780511754067
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 resources management, 30(1), 375–391.
https://doi.org/10.1007/s11269-015-1167-8
Ghobadi, F., & Kang, D. (2023). Application of machine learning in water resources management: a systematic literature review.
Water 15(4), 620.
https://doi.org/10.3390/w1504062
Ghadami, G., & Poosti, M. (2020). Geochemistry, mineralogy and petrogenesis of Rudan Ultramafics North(Groom mountain), Hormozgan province.
Scientific Quarterly Journal of Geosciences,
29(115), 313-324.
https://doi.org/10.22071/gsj.2018.111764.1354 (In Persian)
Planning and Budget Organization. (2019). Hormozgan Management and Planning Organization Land planning, supervision and planning affairs. Center for Development Research and Foresight. (pp. 54).
Haas.J., Metz. M., & Neteler, M. (2021).
MODIS NDVI and EVI, 16-day time series for Europe at 1 km resolution. https://doi.org/10.5281/zenodo.657385
Huang, X., Gao, L., Crosbie, R.S., Zhang, N., Fu, G., & Doble, R. (2019). Groundwater recharge prediction using linear regression, multi-layer perception network, and deep learning.
Water, 11(9), 1879.
https://doi.org/10.3390/w11091879
Jena, M., & Dehuri, S. (2020). Decision tree for classification and regression: A state-of-the art review.
Informatica Journal, 44(4).
https://doi.org/10.31449/inf.v44i4.3023
Kochhar, A., Singh, H., Sahoo, S., Litoria, P.K., & Pateriya, B. (2022). Prediction and forecast of pre-monsoon and post-monsoon groundwater level: Using deep learning and statistical modelling.
Modeling Earth Systems and Environment, 8(2), 2317–2329.
https://doi.org/10.1007/s40808-021-01235-z
Kadra, A., Lindauer, M., Hutter, F., & Grabocka, J. (2021). Well-tuned Simple Nets Excel on Tabular Datasets. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. W. Vaughan (Eds.).
Advances in Neural Information Processing Systems. 34, pp. 23928–23941. Curran Associates, Inc.
https://proceedings.neurips.cc/paper_files/paper/2021/file/c902b497eb972281fb5b4e206db38ee6-Paper.pdf
Kim,G.B. (2020). A study on the establishment of groundwater protection area around a saline waterway by combining artificial neural network and GIS-based AHP.
Environmental Earth Sciences Journal, 79 (5), 1–17.
https://doi.org/10.1007/s12665-020-8862-3
Khan, J., Lee, E., Balobaid, A.S., & Kim, K. (2023). A comprehensive review of conventional, machine leaning, and deep learning models for groundwater level (GWL) forecasting.
Applied Sciences, 13, 2743.
https://doi.org/10.3390/app13042743
Lo, W.C., Borja, R.I., Deng, J.H., & Lee, J.W. (2020). Analytical solution of soil deformation and fluid pressure change for a two-layer system with an upper unsaturated soil and a lower saturated soil under external loading.
Journal of Hydrology, 588, 124997.
https://doi.org/10.1016/j.jhydrol.2020.124997
Manna, T., & Anitha, A. (2023). Deep ensemble-based approach using randomized low-rank approximation for sustainable groundwater level prediction.
Applied Sciences, 13(5), 3210.
https://doi.org/ 10.3390 /app13053210
Malakar, P., Mukherjee, A., Bhanja, S. N., Ray, R. K., Sarkar, S., & Zahid, A. (2021). Machine-learning-based regional-scale groundwater level prediction using GRACE.
Hydrogeology Journal, 29, 1027–1042.
https://doi.org/10.1007/s10040-021-02306-2
Michael, W.J., Minsker, B.S., Tcheng, D., Valocchi, A.J., & Quinn, J.J. (2005). Integrating data sources to improve hydraulic head predictions: A hierarchical machine learning approach.
Water Resources Research, 41, 1–14.
https://doi.org/10.1029/2003WR002802
Miotto, R., Wang, F., Wang, S. Jiang, X., & Dudley, J.T. (2018). Deep learning for healthcare: Review, opportunities and challenges.
Brief. Bioinform, 19(6), 1236–1246.
https://doi.org/10.1093/bib/bbx044
Nguyen, P.T., Ha, D.H., Jaafari, Nguyen, H.D., Van Phong, T., Al-Ansari, N., Prakash, I., Le, H.V., & Pham, B.T. (2020). Groundwater potential mapping combining artificial neural network and real adaboost ensemble technique: the daknong province case-study, Vietnam.
International Journal of Environmental Research and Public Health, 17 (7), 2473.
https://doi.org/10.3390/ijerph17072473
Nadiri, A.A., Chitsazan, N., Tsai, F.T.C., & Moghaddam, A.A. (2014). Bayesian artificial intelligence model averaging for hydraulic conductivity estimation.
Journal of Hydrologic Engineering, 19, 520–532.
https://doi.org/10.1061/(asce)he.1943-5584.0000824
Paroon, S., Yavari, G., & Rezazadeh, M. (2019). Climate classification of Hormozgan province using classical methods. Geography (Regional Planning), 8(33), 115-127. (In Persian)
Priyan, K. (2021). Issues and challenges of groundwater and surface water management in semi-arid regions.
Groundwater Resources Development and Planning in the Semi-Arid Region, 1–17.
https://doi.org/10.1007/978-3-030-68124-11
Pham, B.T., Jaafari, A. Prakash, I., Singh, S.K., Quoc, N.K., & Bui, D.T. (2019). Hybrid computational intelligence models for groundwater potential mapping.
Catena Journal, 182, 104101.
https://doi.org/10.1016/j.catena.2019.104101
Pham, Q. B.,
Kumar, M.,
Nunno, F. D.,
Elbeltagi, A.,
Granata, F.,
Towfiqul Islam, A. R. M. d.,
Talukdar, S.,
Nguyen, X. C.,
Ahmed, A.N., &
Duong Tran Anh, D. (2022). Groundwater level prediction using machine learning algorithms in a drought-prone area.
Neural Computing and Applications, 34, 10751–10773.
https://doi.org/10.1007/s00521-022-07009-7
Rahmati, O., & Melesse, A.M. (2016). Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran. Science of Total Environment, 568, 1110–1123 https://doi.org/10.1016/j.scitotenv.2016.06.176
Rajaee, T., Ebrahimi, H. & Nourani, V. (2019). A review of the artificial intelligence methods in groundwater level modeling.
Journal of hydrology, 572, 336–351.
https://doi.org/10.1016/j.jhydrol.2018.12.037
Rathinasamy, M., Khosa, R., Adamowski, J., Ch, S., Partheepan, G., & Anand, J. (2014). Wavelet-based multiscale performance analysis: an approach to assess and improve hydrological models.
Water Resources Research, 50, 9721–9737.
https://doi.org/10.1002/2013wr014650
Radhakrishnan, N., & Pillai, A. (2020). Comparison of Water Quality Classification Models using Machine Learning.
5th International Conference on Communication and Electronics Systems (ICCES), 1183–1188.
https://doi.org/10.1109/ICCES48766 .2020.9137903
Sahoo, A. K., Pradhan, C., Barik, R. K., & Dubey, H. (2019). DeepReco: Deep Learning Based Health Recommender System Using Collaborative Filtering.
Computation, 7(2), 25.
https://doi.org/10.3390/computation7020025
Sun, J., Hu, L., Li, D., Sun, K., & Yang, Z. (2022). Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management.
Journal of Hydrology, 608, 127630.
https://doi.org/10.1016/j.jhydrol.2022.127630
Singh, S. K., Shirzadi, A., & Pham, B. T. (2021). Application of artificial intelligence in predicting groundwater contaminants. Water Pollut.
International Journal of Management Practice, 71–105.
https://doi.org/10.1007/978-981-15-8358-2_4
Singh, A., Patel, S., Bhadani, V., Kumar, V., & Gaurav, K. (2024). AutoML-GWL: automated machine learning model for the prediction of groundwater level.
Engineering Applications of Artificial Intelligence, 127, 107405.
https://doi.org/10.1016/j.engappai.2023.107405
Salamat, A., Ardestani, M., & Malekmohammadi, B. (2023). Modeling Groundwater Potential Using Machine Learning Models.
Watershed Management Research, 36(4), 114-132.
https://doi.org/10.22092/wmrj.2023.360675.1506 (In Persian)
Tao, H., Hameed, M . M., Marhoon, H. A., Zounemat-Kermani, M., Heddam, S., Kim, S., Sulaiman, S. O., Tan, M. L., Sa’adi, Z., Danandeh Mehr, A., Allawi, M .F., Abba, S.I., Mohamad Zain, M., Falah, M.V., Jamei, M., Bokde, N. D., Bayatvarkeshi, M., Al-Mukhtar, M., Bhagat, S. K., Tiyasha, T., Khedher, K. M., Al-Ansari, N., Shahid, S., & Yaseen, Z. M. (2022b). Groundwater level prediction using machine learning models: a comprehensive review.
Neurocomputing Journal, 489, 271–308.
https://doi.org/10.1016/j.neucom.2022.03.014
Tran, D.H., & Wang, S.J. (2020). Land subsidence due to groundwater extraction and tectonic activity in Pingtung Plain, Taiwan.
Proceedings of the International Association of Hydrological Sciences, 382, 361–365.
https://doi.org/10.5194/piahs-382-361-2020
Wu, T.H., Chen, P.Y., Chen, C.C., Chung, M.J., Ye, Z.K., & Li, M.H. (2024). Classification and Regression Tree (CART)-based estimation of soil water content based on meteorological inputs and explorations of hydrodynamics behind.
Agricultural Water Management, 299, 108869.
https://doi.org/10.1016/J.AGWAT.2024.108869
Yonghong, S. (2013). Response of NDVI to groundwater level change in the lower reaches of the Heihe River, China.
Journal of Desert Research,
33(2),
574-582.
https://doi.org/10.7522/j.issn.1000-694X.2013.00079
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R.X. (2019). Deep learning and its applications to machine health monitoring.
Mechanical Systems and Signal Processing,
115, 213–237.
https://doi.org/10.1016/j.ymssp.2018.05.050
Zhang, J., Zhang, X., Niu, J., Hu, B. X., Soltanian, M. R., Qiu, H., & Yang, L.( 2019). Prediction of groundwater level in seashore reclaimed land using wavelet and artificial neural network-based hybrid model,
Journal of Hydrology, 577, 123948.
https://doi.org/10.1016/j.jhydrol.2019.123948
Zhou, Z., Zhao, L., Lin, A., Qin,W., Lu, Y., Li, J., Zhong, Y., & He, L. (2021). Exploring the potential of deep factorization machine and various gradient boosting models in modeling daily reference evapotranspiration in China.
Arabian Journal of Geosciences., 13, 1287.
https://doi.org/10.1007 /s12517-020-06293-8
Zarei, M., Zandi, R., & Naemitabar, M. (2022). Assessment of flood occurrence potential using data mining models of support vector machine, Chaid and Random Forest (Case study: Frizi watershed).
Journal of Watershed Management Research,
13(25), 133-144.
https://doi.org/10.52547/jwmr.13.25.133 (In Persian)
