Abstract
The delineation of favourable areas of water potentials and their management must be based on rigorous scientific studies. Thus, geographic information system (GIS) and remote sensing (RS) techniques are extremely important in predicting and mapping favourable groundwater zones. This paper aims to map potential areas of groundwater in Waddai region, eastern Chad. A region which has experienced successive droughts over the last two decades, causing the drying of most of the rivers in the area. This study focuses on combining GIS, RS, and analytical hierarchy process; in addition to the factors controlling the movement and retention of groundwater. Six factors (rainfall, slope, land use/land cover, drainage density, lineament density, and lithology) were used to integrate the spatial analysis of areas likely to hold groundwater. The results indicate that potential groundwater areas are unevenly distributed throughout the study area. For instance, the northwestern part is characterized by a low groundwater potential. This low potential in this part of the study area as well as a small portion of the eastern area is explained by a low density of lineaments and drainage, the presence of moderate precipitations, and a semi-permeable lithology (alternating hard rocks and loose sediments). While every low and moderate area occupy most of the middle of the region, good ground water reservoirs occupy a large part of the region. This distribution is explained by good fracturing, permeability, lineament density, high drainage, gentle slope, and precipitation. Therefore, areas of the northwestern part are highly suitable for groundwater exploration and exploitation. Hence; these results would be a guide for future explorations and will maximizes the economic efficiency of the ground water exploitation processes. Furthermore, this map will be useful as a guide in decision-making and on water policy planning.
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References
Abrams, W., Ghoneim, E., Shew, R., et al. (2018). Delineation of groundwater potential (GWP) in the northern United Arab Emirates and Oman using geospatial technologies in conjunction with Simple Additive Weight (SAW), Analytical Hierarchy Process (AHP), and Probabilistic Frequency Ratio (PFR) techniques. Journal of Arid Environments, 157, 77–96. https://doi.org/10.1016/J.JARIDENV.2018.05.005.
Acharya, T., Nag, S. K., & Basumallik, S. (2012). Hydraulic significance of fracture correlated lineaments in precambrian rocks in Purulia district, West Bengal. Journal Geological Society of India, 80(5), 723–730. https://doi.org/10.1007/s12594-012-0198-5.
Adiat, K. A. N., Nawawi, M. N. M., & Abdullah, K. (2012). Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool – A case of predicting potential zones of sustainable groundwater resources. Journal of Hydrology, 440–441, 75–89. https://doi.org/10.1016/J.JHYDROL.2012.03.028.
Afshar, A., Abedi, M., Norouzi, G.-H., & Riahi, M.-A. (2015). Geophysical investigation of underground water content zones using electrical resistivity tomography and ground penetrating radar: A case study in Hesarak-Karaj, Iran. Engineering Geology, 196, 183–193. https://doi.org/10.1016/J.ENGGEO.2015.07.022.
Agarwal, Garg, P. K. (2016). Remote sensing and gis based groundwater potential & recharge zones mapping using multi-criteria decision making technique. Water Resource Management, 30(1), 243–260. https://doi.org/10.1007/s11269-015-1159-8.
Aladejana, O. O., Anifowose, A. Y. B., & Fagbohun, B. J. (2016). Testing the ability of an empirical hydrological model to verify a knowledge-based groundwater potential zone mapping methodology. Modeling Earth Systems and Environment, 2(4), 1–17. https://doi.org/10.1007/s40808-016-0234-3.
Al-Djazouli, M. O., Elmorabiti, K., Zoheir, B., et al. (2019). Use of Landsat-8 OLI data for delineating fracture systems in subsoil regions: Implications for groundwater prospection in the Waddai area, eastern Chad. Arabian Journal of Geosciences, 12(7), 241. https://doi.org/10.1007/s12517-019-4354-8.
Ali, S. A., & Ahmad, A. (2019). Analysing water-borne diseases susceptibility in Kolkata Municipal Corporation using WQI and GIS based Kriging interpolation. GeoJournal, 84, 1–24. https://doi.org/10.1007/s10708-019-10015-3.
Alqahtani, F., & Qaddah, A. A. (2019). GIS digital mapping of flood hazard in Jeddah–Makkah region from morphometric analysis. Arabian Journal of Geosciences, 12(6), 199. https://doi.org/10.1007/s12517-019-4338-8.
Alwan, I. A., Karim, H. H., Aziz, N. A., et al. (2019). Groundwater aquifer suitability for irrigation purposes using multi-criteria decision approach in Salah Al-Din Governorate/Iraq. AgriEngineering, 1(2), 303–323. https://doi.org/10.3390/agriengineering1020023.
Chen, W., Tsangaratos, P., Ilia, I., et al. (2019). Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods. Science of the Total Environment, 684, 31–49. https://doi.org/10.1016/j.scitotenv.2019.05.312.
Chenini, I., Ben, Mammou A., & El May, M. (2010). Groundwater recharge zone mapping using GIS-based multi-criteria analysis: A case study in Central Tunisia (Maknassy Basin). Water Resource Management, 24(5), 921–939. https://doi.org/10.1007/s11269-009-9479-1.
Chowdhury, A., Jha, M. K., Chowdary, V. M., & Mal, B. C. (2009). 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. https://doi.org/10.1080/01431160802270131.
Dano, U. L., Balogun, A.-L., Abubakar, I. R., & Aina, Y. A. (2019). Transformative urban governance: Confronting urbanization challenges with geospatial technologies in Lagos, Nigeria. GeoJournal. https://doi.org/10.1007/s10708-019-10009-1.
Day-Lewis, F. D., Slater, L. D., Robinson, J., et al. (2017). An overview of geophysical technologies appropriate for characterization and monitoring at fractured-rock sites. Journal of Environmental Management, 204, 709–720. https://doi.org/10.1016/J.JENVMAN.2017.04.033.
Díaz-Alcaide, S., Martínez-Santos, P. (2019). Review: Advances in groundwater potential mapping. Hydrogeology Journal, 27(7), 2307–2324.
Duan, H., Deng, Z., Deng, F., & Wang, D. (2016). Assessment of groundwater potential based on multicriteria decision making model and decision tree algorithms. Mathematical Problems in Engineering. https://doi.org/10.1155/2016/2064575.
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. https://doi.org/10.1016/J.ASEJ.2014.08.008.
Fallahpour, A., Olugu, E. U., & Musa, S. N. (2017). A hybrid model for supplier selection: Integration of AHP and multi expression programming (MEP). Neural Computing and Applications, 28(3), 499–504. https://doi.org/10.1007/s00521-015-2078-6.
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. https://doi.org/10.1007/s10661-018-6507-8.
Hajkowicz, S., & Collins, K. (2007). A review of multiple criteria analysis for water resource planning and management. Water Resource Management, 21(9), 1553–1566. https://doi.org/10.1007/s11269-006-9112-5.
Hanjra, M. A., & Qureshi, M. E. (2010). Global water crisis and future food security in an era of climate change. Food Policy, 35(5), 365–377. https://doi.org/10.1016/j.foodpol.2010.05.006.
Horton, R. E. (1932). Drainage-basin characteristics. Transactions of the American Geophysical Union, 13(1), 350. https://doi.org/10.1029/TR013i001p00350.
Hossein, A., Ardakani, H., & Ekhtesasi, M. R. (2016). Groundwater potentiality through analytic hierarchy process (AHP) using remote sensing and geographic information system (GIS). JGeope, 6(1), 75–88.
Jhariya, D. C., Kumar, T., Dewangan, R., et al. (2017). Assessment of groundwater quality index for drinking purpose in the Durg district, Chhattisgarh using Geographical Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) techniques. Journal Geological Society of India, 89(4), 453–459. https://doi.org/10.1007/s12594-017-0628-5.
Jhariya, D. C., Kumar, T., & Pandey, H. K. (2018). Watershed prioritization based on soil and water hazard model using remote sensing, geographical information system and multi-criteria decision analysis approach. Geocarto International, 35(2), 188–208. https://doi.org/10.1080/10106049.2018.1510039.
Kalaivanan, K., Gurugnanam, B., Suresh, M., et al. (2019). Geoelectrical resistivity investigation for hydrogeology conditions and groundwater potential zone mapping of Kodavanar sub-basin, southern India. Sustainable Water Resource Management, 5(3), 1281–1301. https://doi.org/10.1007/s40899-019-00305-6.
Kelleher, J., Mac, Namee B., & D’arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies. Cambridge: MIT Press.
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(8), 1511–1527. https://doi.org/10.1007/s10040-012-0894-7.
Lobo F de L, Souza-Filho PWM, Novo EML de M et al. (2018). Mapping mining areas in the Brazilian Amazon using MSI/sentinel-2 imagery (2017). Remote Sensing, 10(8), 1178. https://doi.org/10.3390/rs10081178.
Machiwal, D., Jha, M. K., & Mal, B. C. (2011). Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques. Water Resource Management, 25(5), 1359–1386. https://doi.org/10.1007/s11269-010-9749-y.
Mahdavi, M., Zeinivand, H., Pourghasemi, H. R., et al. (2014). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8(9), 7059–7071. https://doi.org/10.1007/s12517-014-1668-4.
Mitchell, T. M. (1997). Machine learning. New York: McGraw-Hil.
Moglen, G. E., Eltahir, E. A. B., & Bras, R. L. (1998). On the sensitivity of drainage density to climate change. Water Resources Research, 34(4), 855–862. https://doi.org/10.1029/97WR02709.
Mohammadi-Behzad, H. R., Charchi, A., Kalantari, N., et al. (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. https://doi.org/10.1007/s13146-018-0420-7.
Mondal, N. C., Singh, V. P., & Ahmed, S. (2013). Delineating shallow saline groundwater zones from Southern India using geophysical indicators. Environmental Monitoring and Assessment, 185(6), 4869–4886. https://doi.org/10.1007/s10661-012-2909-1.
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. https://doi.org/10.1080/01431160802468255.
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. https://doi.org/10.1007/s10661-015-5049-6.
Naghibi, S. A., Vafakhah, M., Hashemi, H., et al. (2019). Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Natural Resources Research. https://doi.org/10.1007/s11053-019-09530-4.
Nithya, C. N., Srinivas, Y., Magesh, N. S., & Kaliraj, S. (2019). Assessment of groundwater potential zones in Chittar basin, Southern India using GIS based AHP technique. Remote Sensing Applications: Society and Environment, 15, 100248. https://doi.org/10.1016/J.RSASE.2019.100248.
Panahi, M. R., Mousavi, S. M., & Rahimzadegan, M. (2017). Delineation of groundwater potential zones using remote sensing, GIS, and AHP technique in Tehran–Karaj plain, Iran. Environmental Earth Sciences, 76(23), 1–15. https://doi.org/10.1007/s12665-017-7126-3.
Pinto, D., Shrestha, S., Babel, M. S., & Ninsawat, S. (2017). Delineation of groundwater potential zones in the Comoro watershed, Timor Leste using GIS, remote sensing and analytic hierarchy process (AHP) technique. Applied Water Science, 7(1), 503–519. https://doi.org/10.1007/s13201-015-0270-6.
Rahmati, O., Nazari Samani, A., Mahdavi, M., et al. (2015). Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, 8(9), 7059–7071. https://doi.org/10.1007/s12517-014-1668-4.
Rahmati, O., Pourghasemi, H. R., & Melesse, A. M. (2016). Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. CATENA, 137, 360–372. https://doi.org/10.1016/J.CATENA.2015.10.010.
Razandi, Y., Pourghasemi, H. R., Neisani, N. S., & Rahmati, O. (2015). Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Science Informatics, 8(4), 867–883. https://doi.org/10.1007/s12145-015-0220-8.
Saaty, T. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw-Hill.
Saaty, T. L., & Vargas, L. G. (1984). Inconsistency and rank preservation. Journal of Mathematical Psychology, 28(2), 205–214. https://doi.org/10.1016/0022-2496(84)90027-0
Selvam, S., Dar, F. A., Magesh, N. S., et al. (2016). Application of remote sensing and GIS for delineating groundwater recharge potential zones of Kovilpatti Municipality, Tamil Nadu using IF technique. Earth Science Informatics, 9(2), 137–150. https://doi.org/10.1007/s12145-015-0242-2.
Shailaja, G., Kadam, A. K., Gupta, G., et al. (2019). Integrated geophysical, geospatial and multiple-criteria decision analysis techniques for delineation of groundwater potential zones in a semi-arid hard-rock aquifer in Maharashtra, India. Hydrogeology Journal, 27(2), 639–654. https://doi.org/10.1007/s10040-018-1883-2.
Solomon, S., & Quiel, F. (2006). Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea. Hydrogeology Journal, 14(6), 1029–1041. https://doi.org/10.1007/s10040-006-0096-2.
Su, L. J., Xu, X. Q., Geng, X. Y., & Liang, S. Q. (2017). An integrated geophysical approach for investigating hydro-geological characteristics of a debris landslide in the Wenchuan earthquake area. Engineering Geology, 219, 52–63. https://doi.org/10.1016/j.enggeo.2016.11.020.
Thakur, D., Bartarya, S. K., & Nainwal, H. C. (2018). Mapping groundwater prospect zones in an intermontane basin of the Outer Himalaya in India using GIS and remote sensing techniques. Environmental Earth Sciences, 77(10), 368. https://doi.org/10.1007/s12665-018-7552-x.
Thirumalaivasan, D., Karmegam, M., & Venugopal, K. (2003). AHP-DRASTIC: Software for specific aquifer vulnerability assessment using DRASTIC model and GIS. Environmental Modelling and Software, 18(7), 645–656. https://doi.org/10.1016/S1364-8152(03)00051-3.
Travaglia, C. A., & Dainelli, N. (2003). Groundwater search by remote sensing: A methodological approach. Environment and Natural Resources Research Service Sustainable Development Department 34.
Water.org. (2017). Water.org/our-impact/water-crisis. In water.org/our-impact/water-crisis. https://water.org/subscribe/thanks/. Retrieved December 31, 2018.
Zheng, H., Du, P., Chen, J., et al. (2017). Performance evaluation of downscaling sentinel-2 imagery for land use and land cover classification by spectral-spatial features. Remote Sensing, 9(12), 1274. https://doi.org/10.3390/rs9121274.
Zoheir, B., & Emam, A. (2014). Field and ASTER imagery data for the setting of gold mineralization in Western Allaqi-Heiani belt, Egypt: A case study from the Haimur deposit. Journal of African Earth Sciences, 99, 150–164. https://doi.org/10.1016/J.JAFREARSCI.2013.06.006.
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Al-Djazouli, M.O., Elmorabiti, K., Rahimi, A. et al. Delineating of groundwater potential zones based on remote sensing, GIS and analytical hierarchical process: a case of Waddai, eastern Chad. GeoJournal 86, 1881–1894 (2021). https://doi.org/10.1007/s10708-020-10160-0
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DOI: https://doi.org/10.1007/s10708-020-10160-0