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Research Article

Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping

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Article: 2216852 | Received 15 Feb 2023, Accepted 17 May 2023, Published online: 01 Jun 2023

References

  • Ahmad I, Dar MA, Teka AH, Teshome M, Andualem TG, Teshome A, Shafi T. 2020. GIS and fuzzy logic techniques-based demarcation of groundwater potential zones: a case study from Jemma River basin, Ethiopia. J African Earth Sci. 169:103860.
  • Ahmad MN, Shao Z, Aslam RW, Ahmad I, Liao M, Li X, Song Y. 2022. Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan. Stoch Environ Res Risk Assess. 36(12):4041–4056.
  • Ali S, Khorrami B, Jehanzaib M, Tariq A, Ajmal M, Arshad A, Shafeeque M, Dilawar A, Basit I, Zhang L, et al. 2023. Spatial downscaling of GRACE data based on XGBoost model for improved understanding of hydrological droughts in the Indus Basin Irrigation System (IBIS). Remote Sens. [Internet].15(4):873.
  • Arabameri A, Lee S, Tiefenbacher JP, Ngo PTT. 2020. Novel ensemble of MCDM-artificial intelligence techniques for groundwater-potential mapping in arid and semi-arid regions (Iran). Remote Sens. 12(3):490.
  • Arabameri A, Rezaei K, Cerda A, Lombardo L, Rodrigo-Comino J. 2019. GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci Total Environ. 658:160–177.
  • Azra A, Kaleemullah M, Khattak B, Asma N, Safi AUR, Qaiser J, Afzal M, Tahir U, Sindhu ZUD, Farhan Y. 2019. Comparative efficacy of domestic garlic (Allium sativum) and neem (Azadirachta indica) against Haemonchus contortus in small ruminants. Appl Ecol Env Res. 17(5):10389–10397.
  • Baloch MYJ, Zhang W, Chai J, Li S, Alqurashi M, Rehman G, Tariq A, Talpur SA, Iqbal J, Munir M, et al. 2021. Shallow groundwater quality assessment and its suitability analysis for drinking and irrigation purposes. Water (Switzerland). 13(23):3361.
  • Barakat A, Rafai M, Mosaid H, Islam MS, Saeed S. 2023. Mapping of water-induced soil erosion using machine learning models: a case study of Oum Er Rbia Basin (Morocco). Earth Syst Environ. 7(1):151–170.
  • Basharat M u, Khan JA, Khalil U, Tariq A, Aslam B, Li Q. 2022. Ensuring earthquake-proof development in a swiftly developing region through neural network modelling of earthquakes using nonlinear spatial variables. Buildings. 12(10):1713.
  • Bonham-Carter GF, Agterberg FP, Wright DF. 1989. Integration of geological datasets for gold exploration in Nova Scotia. Introd Readings Geogr Inf Syst. 1:170–182.
  • Bui DT, Shirzadi A, Chapi K, Shahabi H, Pradhan B, Pham BT, Singh VP, Chen W, Khosravi K, Ahmad BB, et al. 2019. A hybrid computational intelligence approach to groundwater spring potential mapping. Water. 11(10):2013.
  • Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, et al. 2018. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ. 634:853–867.
  • da Silva Monteiro L, de Oliveira-Júnior JF, Ghaffar B, Tariq A, Qin S, Mumtaz F, Correia Filho WLF, Shah M, da Rosa Ferraz Jardim AM, da Silva MV, et al. 2022. Rainfall in the urban area and its impact on climatology and population growth. Atmosphere (Basel). 13(10):1610.
  • Eid MH, Elbagory M, Tamma AA, Gad M, Elsayed S, Hussein H, Moghanm FS, Omara AED, 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.
  • Elmoulat M, Ait Brahim L, Mastere M, Ilham Jemmah A. 2015. Mapping of mass movements susceptibility in the zoumi region using satellite image and GIS technology (Moroccan Rif). Int J Sci Eng Res. 6(2):210–217.
  • Falah F, Ghorbani Nejad S, Rahmati O, Daneshfar M, Zeinivand H. 2017. Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods. Geocarto Int. 32(10):1069–1089.
  • Fayez L, Pazhman D, Pham BT, Dholakia MB, Solanki HA, Khalid M, Prakash I. 2018. Application of frequency ratio model for the development of landslide susceptibility mapping at part of Uttarakhand State, India. Int J Appl Eng Res. 13(9):6846–6854.
  • Firdaus R. 2014. Doctoral dissertation assessing land use and land cover change toward sustainability in humid tropical watersheds, indonesia assessing land use and land cover change toward sustainability in humid tropical watersheds, Indonesia. (March):0–1.
  • Guru B, Seshan K, Bera S. 2017. Frequency ratio model for groundwater potential mapping and its sustainable management in cold desert, India. J King Saud Univ – Sci. 29(3):333–347.
  • Hussain MA. 2014. Seroprevalence of Brucellosis in sheep and humans in District Kohat, Pakistan. Adv Anim Vet Sci. 2(9):516–523.
  • Hussain SA, Han FQ, Ma Z, Hussain A, Mughal MS, Han J, Alhassan A, Widory D. 2021. Origin and evolution of eocene rock salts in Pakistan and implications for paleoclimate studies: insights from chemistry and Cl stable isotopes. Front Earth Sci. 9(April):1–13.
  • Hussain H, Zhang S. 2018. Structural evolution of the Kohat Fold and Thrust Belt in the Shakardarra Area (South Eastern Kohat, Pakistan). Geosci. 8(9):311.
  • Islam F, Riaz S, Ghaffar B, Tariq A, Shah SU, Nawaz M, Hussain ML, Amin NU, Li Q, Lu L, et al. 2022. Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modelling. Front Environ Sci. 10(October):1–18.
  • Israil M, Al-Hadithi M, Singhal DC. 2006. Application of a resistivity survey and geographical information system (GIS) analysis for hydrogeological zoning of a piedmont area, Himalayan foothill region, India. Hydrogeol J. 14(5):753–759.
  • Jaiswal RK, Mukherjee S, Krishnamurthy J, Saxena R. 2003. Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development – an approach. Int J Remote Sens. 24(5):993–1008.
  • Jha MK, Chowdary VM, Chowdhury A. 2010. Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeol J. 18(7):1713–1728.
  • Ji Y, Dong C, Kong D, Lu J, Zhou Q. 2015. Heat-activated persulfate oxidation of atrazine: implications for remediation of groundwater contaminated by herbicides. Chem Eng J. 263:45–54.
  • Kalantar B, Pradhan B, Amir Naghibi S, Motevalli A, Mansor S. 2018. Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Hazards Risk. 9(1):49–69.
  • Kaliraj S, Chandrasekar N, Magesh NS. 2014. Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arab J Geosci. 7(4):1385–1401.
  • Karimi-Rizvandi S, Goodarzi HV, Afkoueieh JH, Chung IM, Kisi O, Kim S, Linh NTT. 2021. Groundwater-potential mapping using a self-learning bayesian network model: a comparison among metaheuristic algorithms. Water. 13(5):658.
  • Keesstra SD, Geissen V, Mosse K, Piiranen S, Scudiero E, Leistra M, van Schaik L. 2012. Soil as a filter for groundwater quality. Curr Opin Environ Sustain. 4(5):507–516.
  • Kordestani MD, Naghibi SA, Hashemi H, Ahmadi K, Kalantar B, Pradhan B. 2019. Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeol J. 27(1):211–224.
  • Lee S, Kim JC, Jung HS, Lee MJ, Lee S. 2017. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics Nat Hazards Risk. 8(2):1185–1203. http://www.tandfonline.com/action/journalInformation?show=aimsScope&journalCode=tgnh20#VsXodSCLRhE.
  • Lee S, Kim YS, Oh HJ. 2012. Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. J Environ Manage. 96(1):91–105.
  • Lee S, Song KY, Kim Y, Park I. 2012. Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeol J. 20(8):1511–1527.
  • Li B, Wang N, Chen J. 2021. GIS-based landslide susceptibility mapping using information, frequency ratio, and artificial neural network methods in Qinghai Province, Northwestern China. Adv Civ Eng. 2021:1–14.
  • Madrucci V, Taioli F, de Araújo CC. 2008. Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, São Paulo State, Brazil. J Hydrol. 357(3-4):153–173.
  • Majeed M, Lu L, Haq SM, Waheed M, Sahito HA, Fatima S, Aziz R, Bussmann RW, Tariq A, Ullah I, et al. 2022. Spatiotemporal distribution patterns of climbers along an abiotic gradient in Jhelum District, Punjab, Pakistan. Forests. 13(8):1244.
  • Manap MA, Sulaiman WNA, Ramli MF, Pradhan B, Surip N. 2013. A knowledge-driven GIS modelling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arab J Geosci. 6(5):1621–1637.
  • Maskooni EK, Naghibi SA, Hashemi H, Berndtsson R. 2020. Application of advanced machine learning algorithms to assess groundwater potential using remote sensing-derived data. Remote Sens. 12(17):2742.
  • Moazzam MFU, Rahman G, Munawar S, Tariq A, Safdar Q, Lee B. 2022. Trends of rainfall variability and drought monitoring using standardized precipitation index in a Scarcely Gauged Basin of Northern Pakistan. Water. 14(7):1132.
  • Mohammadi M, Sharifi A, Hosseingholizadeh M, Tariq A. 2021. Detection of oil pollution using sar and optical remote sensing imagery: a case study of the Persian Gulf. J Indian Soc Remote Sens. 49(10):2377–2385.
  • Muavhi N, Thamaga KH, Mutoti MI. 2022. Mapping groundwater potential zones using relative frequency ratio, analytic hierarchy process and their hybrid models: case of Nzhelele-Makhado area in South Africa. Geocarto Int. 37(21):6311–6330. https://doi.org/10.1080/10106049.2021.1936212.
  • Mumtaz F, Li J, Liu Q, Tariq A, Arshad A, Dong Y, Zhao J, Bashir B, Zhang H, Gu C, et al. 2023. Impacts of green fraction changes on surface temperature and carbon emissions: comparison under forestation and urbanization reshaping scenarios. Remote Sens. 15(3):859.
  • Naghibi SA, Pourghasemi HR, Abbaspour K, Naghibi SA, Pourghasemi HR, Abbaspour K. 2018. A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theor Appl Climatol. 131(3-4):967–984.
  • Nampak H, Pradhan B, Manap MA. 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol. 513:283–300.
  • Pardeshi SD, Autade SE, Pardeshi SS. 2013. Landslide hazard assessment: recent trends and techniques. Springerplus. 2(1):1–11.
  • Park I, Kim Y, Lee S. 2014. Groundwater productivity potential mapping using evidential belief function. Groundwater. 52(S1):201–207.
  • Pham BT, Jaafari A, Prakash I, Singh SK, Quoc NK, Bui DT. 2019. Hybrid computational intelligence models for groundwater potential mapping. Catena. 182:104101.
  • Pourghasemi HR, Rossi M. 2017. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol. 130(1-2):609–633.
  • Qureshi AS, McCornick PG, Sarwar A, Sharma BR. 2010. Challenges and prospects of sustainable groundwater management in the Indus Basin, Pakistan. Water Resour Manage. 24(8):1551–1569.
  • Rahman A. 2008. A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. Appl Geogr. 28(1):32–53.
  • S, Hasan Al-Zuhairy M, Alauldeen Abdulrahman Hasan A, Mezher Shnewer F. 2017. GIS-based frequency ratio model for mapping the potential zoning of groundwater in the Western Desert of Iraq. Int J Sci Eng Res. 8(7):52–65.
  • Sadiq Fareed MM, Raza A, Zhao N, Tariq A, Younas F, Ahmed G, Ullah S, Jillani SF, Abbas I, Aslam M. 2022. Predicting divorce prospect using ensemble learning: support vector machine, linear model, and neural network. Comput Intell Neurosci. 2022:3687598.
  • Seeyan S, Merkel B, Abo R. 2014. Investigation of the relationship between groundwater level fluctuation and vegetation cover by using NDVI for Shaqlawa Basin, Kurdistan Region – Iraq. JGG. 6(3):p187–p187.
  • Shahid S, Nath SK, Maksud Kamal ASM. 2002. GIS integration of remote sensing and topographic data using fuzzy logic for ground water assessment in midnapur district, India. Geocarto Int. 17(3):69–74.
  • Shah SHIA, Jianguo Y, Jahangir Z, Tariq A, Aslam B. 2022. Integrated geophysical technique for groundwater salinity delineation, an approach to agriculture sustainability for Nankana Sahib Area, Pakistan. Geomatics, Nat Hazards Risk. 13(1):1043–1064.
  • Shah SHIA, 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.
  • Shano L, Raghuvanshi TK, Meten M. 2020. Landslide susceptibility evaluation and hazard zonation techniques – a review. Geoenviron Disaster. 7(1):1–19.
  • Shirazi SM, Imran HM, Akib S. 2012. GIS-based DRASTIC method for groundwater vulnerability assessment: a review. J Risk Res. 15(8):991–1011.
  • Siddiqui S, Safi MWA, Tariq A, Rehman NU, Haider SW. 2020. GIS based universal soil erosion estimation in district Chakwal Punjab, Pakistan. IJEEG. 11(2):30–36.
  • Singh LK, Jha MK, Chowdary VM. 2018. Assessing the accuracy of GIS-based multi-criteria decision analysis approaches for mapping groundwater potential. Ecol Indic. 91(March):24–37.
  • Solomon S, Quiel F. 2006. Erratum: groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea. Hydrogeol J. 14(6):1029–1041.
  • Tariq A, Jiango Y, Li Q, Gao J, Lu L, Soufan W, Almutairi KF, Habib-Ur-Rahman M. 2023. Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon. 9(2):e13212.
  • Tariq A, Jiango Y, Lu L, Jamil A, Al-Ashkar I, Kamran M, Sabagh, A, El. 2023. Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars. Geomat. Nat Hazards Risk. 14(1):28.
  • Tariq A, Mumtaz F, Majeed M, Zeng X. 2023. Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan. Environ Monit Assess. 195(1):114.
  • Tariq A, Qin S. 2023. Spatio-temporal variation in surface water in Punjab, Pakistan from 1985 to 2020 using machine-learning methods with time-series remote sensing data and driving factors. Agric Water Manag. 280(February):108228.
  • Tariq A, Shu H. 2020. CA-Markov chain analysis of seasonal land surface temperature and land use landcover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sens. 12(20):3402.
  • Tariq A, Shu H, Kuriqi A, Siddiqui S, Gagnon AS, Lu L, Linh NTT, Pham QB. 2021. Characterization of the 2014 indus river flood using hydraulic simulations and satellite images. Remote Sens. 13(11):2053.
  • Tariq A, Siddiqui S, Sharifi A, Shah SHIA. 2022. Impact of spatio-temporal land surface temperature on cropping pattern and land use and land cover changes using satellite imagery, Hafizabad District, Punjab, Province of Pakistan. Arab J Geosci. 15(11):1045.
  • Tariq A, Yan J, Gagnon AS, 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 Inf Sci. 0(0):1–19.
  • Termeh SVR, Khosravi K, Sartaj M, Keesstra SD, Tsai FT-C, Dijksma R, Pham BT. 2019. Optimization of an adaptive neuro-fuzzy inference system for groundwater potential mapping. Hydrogeol J. 27(7):2511–2534.
  • Wahla SS, Kazmi JH, Sharifi A, Shirazi SA, Tariq A, Joyell Smith H. 2022. Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models. Geocarto Int. 37(27):14963–14982.
  • Wu WY, Lo MH, Wada Y, Famiglietti JS, Reager JT, Yeh PJF, Ducharne A, Yang ZL. 2020. Divergent effects of climate change on future groundwater availability in key mid-latitude aquifers. Nat Commun. 11(1):1–9.
  • Xu C, Xu X, Dai F, Xiao J, Tan X, Yuan R. 2012. Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region. J Earth Sci. 23(1):97–120.
  • Yin H, Shi Y, Niu H, Xie D, Wei J, Lefticariu L, Xu S, Yin H, Shi Y, Niu H, et al. 2018. A GIS-based model of potential groundwater yield zonation for a sandstone aquifer in the Juye Coalfield, Shangdong. China. JHyd. 557:434–447.
  • Zainab N, Tariq A, Siddiqui S. 2021. Development of Web-Based GIS Alert System for Informing Environmental Risk of Dengue Infections in Major Cities of Pakistan. Geos Ind. 6(1):77.
  • Zhu Z, Wu Y, Liang Z. 2022. Mining-induced stress and ground pressure behavior characteristics in mining a thick coal seam with hard roofs. Front Earth Sci. 10(March):1–12.