[1] Al-Abadi AM, Pourghasemi HR, Shahid S, Ghalib HB. Spatial Mapping of Groundwater Potential Using
Entropy Weighted Linear Aggregate Novel Approach and GIS. Arab J Sci Eng. 2017;42(3):1185-99.
[2] AlAyyash S, Al-Fugara Ak, Shatnawi R, Al-Shabeeb AR, Al-Adamat R, Al-Amoush H. Combination of
Metaheuristic Optimization Algorithms and Machine Learning Methods for Groundwater Potential Mapping.
Sustainability. 2023;15(3):2499.
[3] Emami H, Emami S. Presentating a New Approach for Evaluating the Hydro-geochemical Quality of
Groundwater using Swarm Intelligence Algorithms. Iranian journal of Ecohydrology. 2019;6(1):177-90
[Persian].
[4] Ercin AE, Hoekstra AY. Water footprint scenarios for 2050: A global analysis. Environment International.
2014;64:71-82.
[5] Moridi A. State of water resources in Iran. Int J Hydrol. 2017;1:111-4.
[6] Rahmati O, Melesse AM. 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
The Total Environment. 2016;568:1110-23.
[7] Poursalehi F, KhasheiSiuki A, Hashemi SR. Investigating the performance of random forest algorithm in
predicting water table fluctuations Compared with two models of decision tree and artificial neural network
(Case study: unconfined aquifer of Birjand plain). Iranian journal of Ecohydrology. 2021;8(4):961-74
[Persian].
[8] Najib M, Asghari Moghaddam A, Nadiri AA, Fijani E. Evaluating Quality Variation of Groundwater Resources
in Marand Plain Using Unsupervised Combination Approach (GQI and GWQI Index). Iranian journal of
Ecohydrology. 2021;8(4):1061-80 [Persian].
[9] Rahmati O, Naghibi SA, Shahabi H, Bui DT, Pradhan B, Azareh A, et al. Groundwater spring potential
modelling: Comprising the capability and robustness of three different modeling approaches. Journal of
Hydrology. 2018;565:248-61.
[10] Khosravi K, Panahi M, Tien Bui D. Spatial prediction of groundwater spring potential mapping based on an
adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrology and Earth System Sciences.
2018;22(9):4771-92.
[11] Razavi-Termeh SV, Khosravi K, Sadeghi-Niaraki A, Choi S-M, Singh VP. Improving groundwater potential
mapping using metaheuristic approaches. Hydrological Sciences Journal. 2020;65(16):2729-49.
[12] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
[13] Pradhan B, Lee S, Dikshit A, Kim H. Spatial flood susceptibility mapping using an explainable artificial
intelligence (XAI) model. Geoscience Frontiers. 2023;14(6):101625.
[14] Paryani S, Bordbar M, Jun C, Panahi M, Bateni SM, Neale CMU, et al. Hybrid-based approaches for the flood
susceptibility prediction of Kermanshah province, Iran. Nat Hazards. 2023;116(1):837-68.
[15] Falah F, Ghorbani Nejad S, Rahmati O, Daneshfar M, Zeinivand H. Applicability of generalized additive
model in groundwater potential modelling and comparison its performance by bivariate statistical methods.
Geocarto International. 2017;32(10):1069-89.
[16] Al-Fugara Ak, Ahmadlou M, Shatnawi R, AlAyyash S, Al-Adamat R, Al-Shabeeb AA-R, et al. Novel hybrid
models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential
mapping. Geocarto International. 2022;37(9):2627-46.
[17] Kordestani MD, Naghibi SA, Hashemi H, Ahmadi K, Kalantar B, Pradhan B. Groundwater potential mapping
using a novel data-mining ensemble model. 2019.
[18] Pande CB, Moharir KN, Singh SK, Varade AM. An integrated approach to delineate the groundwater
potential zones in Devdari watershed area of Akola district, Maharashtra, Central India. Environ Dev Sustain.
2020;22(5):4867-87.
[19] Ikirri M, Boutaleb S, Ibraheem IM, Abioui M, Echogdali FZ, Abdelrahman K, et al. Delineation of Groundwater
Potential Area using an AHP, Remote Sensing, and GIS Techniques in the Ifni Basin, Western Anti-Atlas,
Morocco. Water. 2023;15(7):1436.
[20] Fashae OA, Tijani MN, Talabi AO, Adedeji OI. Delineation of groundwater potential zones in the crystalline
basement terrain of SW-Nigeria: an integrated GIS and remote sensing approach. Appl Water Sci. 2014;4(1):19-
38.
[21] Li Z, Liu F, Yang W, Peng S, Zhou J. A survey of convolutional neural networks: analysis, applications, and
prospects. IEEE transactions on neural networks and learning systems. 2021;33(12):6999-7019.
[22] Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, et al. Recent advances in convolutional neural networks.
Pattern Recognition. 2018;77:354-77.
[23] Albawi S, Mohammed T, Al-Zawi S. Understanding of a convolutional neural network. In2017 international
conference on engineering and technology (ICET) 2017 Aug 21 (pp. 1-6). Ieee.
[24] Reddy K, Saha AK. A modified Whale Optimization Algorithm for exploitation capability and stability
enhancement. Heliyon. 2022;8(10).
[25] Karaboga D, Akay B. A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and
Computation. 2009;214(1):108-32.
[26] Wang W, Lu Y. Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in
Assessing Rounding Model. IOP Conf Ser: Mater Sci Eng. 2018;324(1):012049.
[27] Waikar M, Nilawar AP. Identification of groundwater potential zone using remote sensing and GIS technique.
International Journal of Innovative Research in Science, Engineering and Technology. 2014;3(5):12163-74.
[28] Lee S, Hong S-M, Jung H-S. GIS-based groundwater potential mapping using artificial neural network and
support vector machine models: the case of Boryeong city in Korea. Geocarto International. 2018;33(8):847-
61.
[29] Mallick J, Khan RA, Ahmed M, Alqadhi SD, Alsubih M, Falqi I, et al. Modeling Groundwater Potential Zone
in a Semi-Arid Region of Aseer Using Fuzzy-AHP and Geoinformation Techniques. Water. 2019;11(12):2656.
[30] Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A. A survey on new generation metaheuristic algorithms.
Computers & Industrial Engineering. 2019;137:106040