Remote Sensing and Machine Learning for Groundwater Spring Potential Assessment in Eastern and Northeastern Iran

 AlAyyash, S., Al-Fugara, A., Shatnawi, R., Al-Shabeeb, A. R., Al-Adamat, R., & Al-Amoush, H. (2023). Combination of metaheuristic optimization algorithms and machine learning methods for groundwater potential mapping. Sustainability, 15(3), 2499.

Al-Djazouli, M. O., Elmorabiti, K., Rahimi, A., Amellah, O., & Fadil, O. A. M. (2021). Delineating of groundwater potential zones based on remote sensing, GIS and analytical hierarchical process: A case of Waddai, eastern Chad. GeoJournal, 86(5), 1881–1894.

Al-Fugara, A., Pourghasemi, H. R., Al-Shabeeb, A. R., Habib, M., Al-Adamat, R., Al-Amoush, H., & Collins, A. L. (2020). A comparison of machine learning models for the mapping of groundwater spring potential. Environmental Earth Sciences, 79, 206. 

Al-Kindi, K. M., & Janizadeh, S. (2022). Machine learning and hyperparameters algorithms for identifying groundwater aflaj potential mapping in semi-arid ecosystems using LiDAR, Sentinel-2, GIS data, and analysis. Remote Sensing, 14(21), 5425.

Arneth, A., Barbosa, H., Benton, T., Calvin, K., Calvo, E., Connors, S., Cowie, A., Davin, E., Denton, F., & van Diemen, R. (2019). Summary for policymakers. In P. R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai, R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira, P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, & J. Malley (Eds.), Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (ISBN 978-92-9169-154-8). Intergovernmental Panel on Climate Change (IPCC).

Arthur, J. D., Wood, H. A. R., Baker, A. E., Cichon, J. R., & Raines, G. L. (2007). Development and implementation of a Bayesian-based aquifer vulnerability assessment in Florida. Natural Resources Research, 16, 93–107.

Austin, M. P., Cunningham, R. B., & Fleming, P. M. (1984). New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio, 55(1), 11–27.

Bhadani, V., Singh, A., Kumar, V., & Gaurav, K. (2023). Machine learning models to predict groundwater level in a semi-arid river catchment, Central India. In EGU General Assembly, Vienna, Austria.

Boughariou, E., Allouche, N., Ben Brahim, F., Nasri, G., & Bouri, S. (2021). Delineation of groundwater potentials of Sfax region, Tunisia, using fuzzy analytical hierarchy process, frequency ratio, and weights of evidence models. Environment, Development and Sustainability, 23(10), 14749–14774.

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth International Group.

Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd International Conference on Machine Learning (ICML ’06) (pp. 161–168). ACM.

Chen, W., Panahi, M., Khosravi, K., et al. (2019). Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. Journal of Hydrology, 572, 435–448.

Chenini, I., Mammou, A. B., & May, M. E. (2010). Groundwater recharge zone mapping using GIS-based multi-criteria analysis: A case study in Central Tunisia (Maknassy Basin). Water Resources Management, 24, 921–939.

Chowdhury, A., Jha, M. K., Chowdary, V. M., & Mal, B. C. (2008). Integrated remote sensing and GIS-based approach for assessing groundwater potential in West Medinipur district, West Bengal, India. International Journal of Remote Sensing, 30(1), 231–250.

Corsini, A., Cervi, F., & Ronchetti, F. (2009). Weight of evidence and artificial neural networks for potential groundwater spring mapping: An application to the Mt. Modino area (Northern Apennines, Italy). Geomorphology, 111, 79–87.

Damaneh, J. M., Ahmadi, J., Rahmanian, S., Sadeghi, S. M. M., Nasiri, V., & Borz, S. A. (2022). Prediction of wild pistachio ecological niche using machine learning models. Ecological Informatics, 69, 101907.

DEP. (1993). Carte hydrogeologique du Burkina Faso. Feuille Ouagadougou. Echelle 1:50000. Ministère de l'Eau and Directorat Général de la Coopération au Développement, Pays-Bas.

Devanantham, A., Subbarayan, S., Singh, L., et al. (2020). GIS-based multi-criteria analysis for identification of potential groundwater recharge zones: A case study from Ponnaniyaru watershed, Tamil Nadu, India. HydroResearch, 3, 1–14.

Díaz-Alcaide, S., & Martínez-Santos, P. (2019). Advances in groundwater potential mapping. Hydrogeology Journal, 27, 2307–2324.

Eid, M. H., Elbagory, M., Tamma, A. A., Gad, M., Elsayed, S., Hussein, H., Moghanm, F. S., Omara, A. E.-D., Kovács, A., & Péter, S. (2023). Evaluation of groundwater quality for irrigation in deep aquifers using multiple graphical and indexing approaches supported with machine learning models and GIS techniques, Souf Valley, Algeria. Water, 15(1), 182.

Elbeih, S. F. (2015). An overview of integrated remote sensing and GIS for groundwater mapping in Egypt. Ain Shams Engineering Journal, 6(1), 1–15.

Elith, J., & Franklin, J. (2013). Species distribution modeling. In S. A. Levin (Ed.), Encyclopedia of biodiversity (2nd ed., pp. 692–705). Elsevier.

Falah, F., & Zeinivand, H. (2019). GIS-based groundwater potential mapping in Khorramabad in Lorestan, Iran, using frequency ratio (FR) and weights of evidence (WoE) models. Water Resources, 46(5), 679–692.

Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24(1), 38–49.

Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.

Galton, F. (1892). Finger prints. Macmillan.

Ghayoumian, J., Mohseni Saravi, M., Feiznia, S., Nouri, B., & Malekian, A. (2007). Application of GIS techniques to determine areas most suitable for artificial groundwater recharge in a coastal aquifer in southern Iran. Journal of Asian Earth Sciences, 30(2), 364–374.

Golkarian, A., Naghibi, S. A., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS. Environmental Monitoring and Assessment, 190(3), 149.

Grönwall, J., & Danert, K. (2020). Regarding groundwater and drinking water access through a human rights lens: Self-supply as a norm. Water, 12(2), 419.

Gupta, M., & Srivastava, P. K. (2010). Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water International, 35(2), 233–245.

Harrell, F. E., Jr., Lee, K. L., & Mark, D. B. (1996). Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15(4), 361–387.

Hastie, T., Tibshirani, R., & Buja, A. (1994). Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89(428), 1255–1270.

Jha, M. K., Chowdhury, A., Chowdary, V. M., & Peiffer, S. (2007). Groundwater management and development by integrated remote sensing and geographic information systems: Prospects and constraints. Water Resources Management, 21(2), 427–467.

Kalantar, B., Al-Najjar, H. A. H., Pradhan, B., Saeidi, V., Halin, A. A., Ueda, N., & Naghibi, S. A. (2019). Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water, 11(9), 1909.

Kamali Maskooni, E., Naghibi, S. A., Hashemi, H., & Berndtsson, R. (2020). Application of advanced machine learning algorithms to assess groundwater potential using remote sensing-derived data. Remote Sensing, 12(17), 2742.

Khan, Z. A., & Jhamnani, B. (2023). Identification of groundwater potential zones of Idukki district using remote sensing and GIS-based machine-learning approach. Water Supply, 23(6), 2426–2446.

Landis, J. R., & Koch, G. G. (1977). An application of hierarchical Kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 33(2), 363–374.

Lee, S., Song, K. Y., Kim, Y., & Park, I. (2012). Regional groundwater productivity potential mapping using a geographic information system (GIS)-based artificial neural network model. Hydrogeology Journal, 20(7), 1511–1527.

Magesh, N. S., Chandrasekar, N., & Soundranayagam, J. P. (2012). Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geoscience Frontiers, 3(2), 189–196.

Martínez-Santos, P., & Renard, P. (2020). Mapping groundwater potential through an ensemble of big data methods. Groundwater, 58(4), 583–597.

Martínez-Santos, P., Díaz-Alcaide, S., De la Hera, A., & Gomez-Escalonilla, V. (2021). A multi-parametric supervised classification algorithm to map groundwater-dependent wetlands. Journal of Hydrology, 603(Part A), 126873.

Martín-Loeches, M., Reyes-López, J., Ramírez-Hernández, J., Temiño-Vela, J., & Martínez-Santos, P. (2018). Comparison of RS/GIS analysis with classic mapping approaches for siting low-yield boreholes for hand pumps in crystalline terrains: An application to rural communities of the Caimbambo province, Angola. Journal of African Earth Sciences, 138, 22–31.

Masoudi, R., Mousavi, S. R., Rahimabadi, P. D., Panahi, M., & Rahmani, A. (2023). Assessing data mining algorithms to predict the quality of groundwater resources for determining irrigation hazard. Environmental Monitoring and Assessment, 195(4), 319.

Mogaji, K. A., & Lim, H. S. (2018). Application of Dempster-Shafer theory of evidence model to geoelectric and hydraulic parameters for groundwater potential zonation. NRIAG Journal of Astronomy and Geophysics, 7(2), 134–148. Moghaddam, D. D., Rahmati, O., Panahi, M., Tiefenbacher, J., Darabi, H., Haghizadeh, A., ... & Bui, D. T. (2020). The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers. Catena, 187, 104421.

Mohammadi-Behzad, H. R., Charchi, A., Kalantari, N., Nejad, A. M., & Vardanjani, H. K. (2019). Delineation of groundwater potential zones using remote sensing (RS), geographical information system (GIS) and analytic hierarchy process (AHP) techniques: A case study in the Leylia–Keynow watershed, southwest of Iran. Carbonates and Evaporites, 34(4), 1307–1319.

Momeni Demaneh, J., Esmaeilpour, Y., Gholami, H., & Farashi, A. (2021). Properly predict the growth of (Ferula assa-foetida L.) in northeastern Iran using the maximum entropy model. Journal of Range and Desert Research of Iran, 28(3), 587–592.

Mumtaz, F., Li, J., Liu, Q., Tariq, A., Arshad, A., Dong, Y., Zhao, J., Bashir, B., Zhang, H., Gu, C., & Liu, C. (2023). Impacts of green fraction changes on surface temperature and carbon emissions: Comparison under forestation and urbanization reshaping scenarios. Remote Sensing, 15(3), 859.

Murthy, K. S. R., & Mamo, A. G. (2009). Multi-criteria decision evaluation in groundwater zones identification in Moyale-Teltele subbasin, South Ethiopia. International Journal of Remote Sensing, 30(11), 2729–2740.

Naghibi, S. A., Ahmadi, K., & Daneshi, A. (2017a). Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31(9), 2761–2775.

Naghibi, S. A., Moghaddam, D. D., Kalantar, B., Pradhan, B., & Kisi, O. (2017b). A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. Journal of Hydrology, 548, 471–483.

Naghibi, S. A., Pourghasemi, H. R., & Dixon, B. (2016). GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environmental Monitoring and Assessment, 188(1), 44.

Naghibi, S. A., Dolatkordestani, M., Rezaei, A., Amouzegari, P., Heravi, M. T., Kalantar, B., & Pradhan, B. (2019). Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential. Environmental Monitoring and Assessment, 191, 1–20.

Nampak, H., Pradhan, B., & Manap, M. A. (2014). Application of GIS-based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283–300.

Nazir, J., Ali, M., Sarwar, A., Khan, S., Rehman, K., Fahim, B., & Iqbal, B. (2024). Delineation and validation of GIS-based groundwater potential zones under arid to semi-arid environment using multi-influence-factors approach. Geology, Ecology, and Landscapes, 1–17.

Nguyen, P. T., Ha, D. H., Avand, M., Jaafari, A., Nguyen, H. D., Al-Ansari, N., Van Phong, T., Sharma, R., Kumar, R., Le, H. V., Ho, L. S., Prakash, I., & Pham, B. T. (2020). Soft computing ensemble models based on logistic regression for groundwater potential mapping. Applied Sciences, 10(7), 2469.

Nix, H. A. (1986). A biogeographic analysis of Australian elapid snakes. Atlas of Elapid Snakes of Australia, 7, 4–15.

Obeidavi, S., Gandomkar, M., Akbarizadeh, G., & Delfan, H. (2021). Evaluation of groundwater potential using Dempster-Shafer model and sensitivity analysis of effective factors: A case study of North Khuzestan Province. Remote Sensing Applications: Society and Environment, 22, 100475.

Oh, H. J., Kim, Y. S., Choi, J. K., Park, E., & Lee, S. (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399, 158–172.

Ozdemir, A. (2011). Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). Journal of Hydrology, 405, 123–136.

Panahi, M., Sadhasivam, N., Pourghasemi, H. R., Rezaie, F., & Lee, S. (2020). Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology. Pourtaghi, Z. S., & Pourghasemi, H. R. (2014). GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeology Journal, 22, 643–662.

Pradhan, A. M. S., Kim, Y. T., Shrestha, S., Huynh, T. C., & Nguyen, B. P. (2021). Application of deep neural network to capture groundwater potential zone in mountainous terrain, Nepal Himalaya. Environmental Science and Pollution Research, 28(15), 18501–18517.

Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine, and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.

Prasad, P., Loveson, V. J., Kotha, M., & Yadav, R. (2020). Application of machine learning techniques in groundwater potential mapping along the west coast of India. GIScience & Remote Sensing, 57(5), 735–752.

Seifu, T. K., Eshetu, K. D., Woldesenbet, T. A., Alemayehu, T., & Ayenew, T. (2023). Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia. Hydrology Research, 54(10), 1246–1266.

Shah, S. H. I. A., Yan, J., Ullah, I., Aslam, B., Tariq, A., Zhang, L., & Mumtaz, F. (2021). Classification of aquifer vulnerability by using the DRASTIC index and geo-electrical techniques. Water, 13(16), 2144.

Singh, L. K., Jha, M. K., & Chowdary, V. M. (2018). Assessing the accuracy of GIS-based multi-criteria decision analysis approaches for mapping groundwater potential. Ecological Indicators, 91, 24–37.

Smeeton, N. C. (1985). Early history of the kappa statistic. Biometrics, 41(3), 795–796.

Srivastava, P. K., & Bhattacharya, A. K. (2006). Groundwater assessment through an integrated approach using remote sensing, GIS and resistivity techniques: A case study from a hard rock terrain. International Journal of Remote Sensing, 27(20), 4599–4620.

Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293.

Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use land cover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), 3402.

Tariq, A., Yan, J., Gagnon, A. S., Riaz Khan, M., & Mumtaz, F. (2022). Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Science, 26(3), 302–320.

Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). BIOMOD – A platform for ensemble forecasting of species distributions. Ecography, 32(3), 369–373.

Walther, G. R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J. C., & Bairlein, F. (2002). Ecological responses to recent climate change. Nature, 416(6879), 389–395.

Yi, Y. J., Cheng, X., Yang, Z. F., & Zhang, S. H. (2016). MaxEnt modeling for predicting the potential distribution of endangered medicinal plant (H. riparia Lour) in Yunnan, China. Ecological Engineering, 92, 260–269.

Yu, H., Wen, X., Wu, M., Sheng, D., Wu, J., & Zhao, Y. (2022). Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture. Agricultural Water Management, 262, 107423.

Zhang, X., Yuan, Y., Zhu, Z., Ma, Q., Yu, H., Li, M., Ma, J., Yi, S., He, X., & Sun, Y. (2021). Predicting the distribution of Oxytropis ochrocephala Bunge in the source region of the Yellow River (China) based on UAV sampling data and species distribution model. Remote Sensing, 13(24), 5129.