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Research Article

Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management

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Article: 2306275 | Received 18 Sep 2023, Accepted 11 Jan 2024, Published online: 13 Feb 2024

References

  • Abijith D, Saravanan S, Singh L, Jennifer JJ, Saranya T, Parthasarathy KSS. 2020. GIS-based multi-criteria analysis for identification of potential groundwater recharge zones - a case study from Ponnaniyaru watershed, Tamil Nadu, India. HydroRes. 3:1–14. doi: 10.1016/j.hydres.2020.02.002.
  • Ahmad K, Banerjee A, Rashid W, Xia Z, Karim S, Asif M. 2022. Assessment of long-term rainfall variability and trends using observed and satellite data in central Punjab, Pakistan. Atmosphere (Basel). 14(1):60. doi: 10.3390/atmos14010060.
  • Al-Abadi AM, Al-Temmeme AA, Al-Ghanimy MA. 2016. A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq. Sustain Water Resour Manag. 2(3):265–283. doi: 10.1007/s40899-016-0056-5.
  • Arulbalaji P, Padmalal D, Sreelash K. 2019. GIS and AHP techniques based delineation of groundwater potential zones: a case study from Southern Western Ghats, India. Sci Rep. 9(1):2082. doi: 10.1038/s41598-019-38567-x.
  • Bai B, Rao D, Chang T, Guo Z. 2019. A nonlinear attachment-detachment model with adsorption hysteresis for suspension-colloidal transport in porous media. J Hydrol. 578:124080. doi: 10.1016/j.jhydrol.2019.124080.
  • Benjmel K, Amraoui F, Aydda A, Tahiri A, Yousif M, Pradhan B, Abdelrahman K, Fnais MS, Abioui M. 2022. A multidisciplinary approach for groundwater potential mapping in a fractured semi-arid terrain (Kerdous Inlier, Western Anti-Atlas, Morocco). Water. 14(10):1553. doi: 10.3390/w14101553.
  • Bera A, Mukhopadhyay BP, Barua S. 2020. Delineation of groundwater potential zones in Karha river basin, Maharashtra, India, using AHP and geospatial techniques. Arab J Geosci. 13(15):1–21. doi: 10.1007/s12517-020-05702-2.
  • Berhanu KG, Hatiye SD. 2020. Identification of groundwater potential zones using proxy data: case study of Megech Watershed, Ethiopia. J Hydrol Reg Stud. 28:100676. doi: 10.1016/j.ejrh.2020.100676.
  • Bonham-Carter G. 1994. Geographic information systems for geoscientists: modelling with GIS. doi: 10.1016/C2013-0-03864-9.
  • 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. doi: 10.3390/w11102013.
  • Cheng Q, Bonham-Carter G, Wang W, Zhang S, Li W, Qinglin X. 2011. A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Comput Geosci. 37(5):662–669. doi: 10.1016/j.cageo.2010.11.001.
  • Cheng Y, Lan S, Fan X, Tjahjadi T, Jin S, Cao L. 2023. A dual-branch weakly supervised learning based network for accurate mapping of woody vegetation from remote sensing images. Int J Appl Earth Obs Geoinf. 124:103499. doi: 10.1016/j.jag.2023.103499.
  • 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. doi: 10.1016/j.scitotenv.2018.04.055.
  • Chen W, Panahi M, Khosravi K, Pourghasemi HR, Rezaie F, Parvinnezhad D. 2019. Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization. J Hydrol. 572:435–448. doi: 10.1016/j.jhydrol.2019.03.013.
  • 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(1-2):79–87. doi: 10.1016/j.geomorph.2008.03.015.
  • Das S, Pardeshi SD. 2018. Integration of different influencing factors in GIS to delineate groundwater potential areas using IF and FR techniques: a study of Pravara basin, Maharashtra, India. Appl Water Sci. 8(7):197. doi: 10.1007/s13201-018-0848-x.
  • Das N, Sutradhar S, Ghosh R, Mondal P, Islam S. 2021. The response of groundwater to multiple concerning drivers and its future: a study on Birbhum District, West Bengal, India. Appl Water Sci. 11(4):79. doi: 10.1007/s13201-021-01410-8.
  • Dong W, Yang Y, Qu J, Xiao S, Li Y. 2023. Local Information-enhanced graph-transformer for hyperspectral image change detection with limited training samples. IEEE Trans Geosci Remote Sens. 61:1–14. doi: 10.1109/TGRS.2023.3269892.
  • Dong W, Zhao J, Qu J, Xiao S, Li N, Hou S, Li Y. 2023. Abundance matrix correlation analysis network based on hierarchical multihead self-cross-hybrid attention for hyperspectral change detection. IEEE Trans Geosci Remote Sens. 61:1–13. doi: 10.1109/TGRS.2023.3235401.
  • Ercanoglu M, Gokceoglu C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geol. 41(6):720–730. doi: 10.1007/s00254-001-0454-2.
  • Farooq S, Akram MS. 2021. Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas. Arab J Geosci. 14(10):1–16. doi: 10.1007/s12517-021-07147-7.
  • Fatema K, Joy MAR, Amin FMR, Sarkar SK. 2023. Groundwater potential mapping in Jashore, Bangladesh. Heliyon. 9(3):e13966. doi: 10.1016/J.HELIYON.2023.E13966.
  • Gerland P, Raftery AE, Sevčíková H, Li N, Gu D, Spoorenberg T, Alkema L, Fosdick BK, Chunn J, Lalic N, et al. 2014. World population stabilization unlikely this century. Science. 346(6206):234–237. doi: 10.1126/SCIENCE.1257469.
  • Getachew N, Meten M. 2021. Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenviron Disasters. 8(1):1–22. doi: 10.1186/S40677-021-00177-Z/FIGURES/11.
  • Ghosh R, Sutradhar S, Mondal P, Das N. 2021. Application of DRASTIC model for assessing groundwater vulnerability: a study on Birbhum district, West Bengal, India. Model Earth Syst Environ. 7(2):1225–1239. doi: 10.1007/s40808-020-01047-7.
  • Golkarian A, Naghibi SA, Kalantar B, Pradhan B. 2018. Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS. Environ Monit Assess. 190(3):1–16. doi: 10.1007/s10661-018-6507-8.
  • 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. doi: 10.1016/j.jksus.2016.08.003.
  • Hasanuzzaman M, Mandal MH, Hasnine M, Shit PK. 2022. Groundwater potential mapping using multi-criteria decision, bivariate statistic and machine learning algorithms: evidence from Chota Nagpur Plateau, India. Appl Water Sci. 12(4):1–16. doi: 10.1007/s13201-022-01584-9.
  • Islam F, Tariq A, Guluzade R, Zhao N, Shah SU, Ullah M, Hussain ML, Ahmad MN, Alasmari A, Alzuaibr FM, et al. 2023. Comparative analysis of GIS and RS based models for delineation of groundwater potential zone mapping. Geomatics, Nat Hazards Risk. 14(1):27. doi: 10.1080/19475705.2023.2216852.
  • Jaafarzadeh MS, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Rouhani H. 2021. Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models. Sci Rep. 11(1):5587. doi: 10.1038/s41598-021-85205-6.
  • Javed T, Sarwar T, Ullah I, Ahmad S, Rashid S. 2019. Evaluation of groundwater quality in district Karak Khyber Pakhtunkhwa, Pakistan. Water Sci. 33(1):1–9. doi: 10.1080/11104929.2019.1626630.
  • Jenks GF. 1967. The data model concept in statistical mapping. Int Yearb Cartogr. 7(1):186–190.
  • 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. doi: 10.3390/w13050658.
  • Keller CP. 1995. Geographic information systems for geoscientists: modelling with GIS. Comput Geosci. 21(9):1110–1112. doi: 10.1016/0098-3004(95)90019-5.
  • Khan MYA, ElKashouty M, Subyani AM, Tian F. 2023. Morphometric determination and digital geological mapping by RS and GIS techniques in Aseer–Jazan contact, Southwest Saudi Arabia. Water. 15(13):2438. doi: 10.3390/w15132438.
  • Khan MYA, ElKashouty M, Subyani AM, Tian F, Gusti W. 2021. GIS and RS intelligence in delineating the groundwater potential zones in arid regions: a case study of southern Aseer, southwestern Saudi Arabia. Appl Water Sci. 12(1):3. doi: 10.1007/s13201-021-01535-w.
  • Khan MY, ElKashouty M, Tian F. 2022. Mapping groundwater potential zones using analytical hierarchical process and multicriteria evaluation in the central Eastern Desert, Egypt. Water. 14(7):1041. doi: 10.3390/w14071041.
  • Khan MYA, ElKashouty M, Zaidi FK, Egbueri JC. 2023. Mapping aquifer recharge potential zones (ARPZ) using integrated geospatial and analytic hierarchy process (AHP) in an arid region of Saudi Arabia. Remote Sens. 15(10):2567. doi: 10.3390/rs15102567.
  • Khan U, Faheem H, Jiang Z, Wajid M, Younas M, Zhang B. 2021. Integrating a GIS-based multi-influence factors model with hydro-geophysical exploration for groundwater potential and hydrogeological assessment: a case study in the Karak Watershed, Northern Pakistan. Water. 13(9):1255. doi: 10.3390/w13091255.
  • Kim D, Cha BG, Yeo IW. 2023. Impact of road embankment construction on groundwater system in alluvial aquifers. Geosci J. 27(1):89–99. doi: 10.1007/S12303-022-0024-Z/METRICS.
  • Kim JC, Jung HS, Lee S. 2018. Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors. J Hydroinformatics. 20(6):1436–1451. doi: 10.2166/hydro.2018.120.
  • Lautz LK, Hoke GD, Lu Z, Siegel DI, Christian K, Kessler JD, Teale NG. 2014. Using discriminant analysis to determine sources of salinity in shallow groundwater prior to hydraulic fracturing. Environ Sci Technol. 48(16):9061–9069. doi: 10.1021/ES502244V.
  • Lee S, Hong SM, Jung HS. 2017. GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto International. 33(8):847–861. doi: 10.1080/10106049.2017.1303091.
  • Lee S, Hong SM, Jung HS. 2018. GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto Int. 33(8):847–861. doi: 10.1080/10106049.2017.1303091.
  • Lee S, Pradhan B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4(1):33–41. doi: 10.1007/s10346-006-0047-y.
  • Li Q, Lu L, Zhao Q, Hu S. 2022. Impact of Inorganic Solutes’ release in groundwater during oil shale in situ exploitation. Water. 15(1):172. doi: 10.3390/w15010172.
  • Li Y, Qian J, Feng S, Chen Q, Zuo C. 2022. Deep-learning-enabled dual-frequency composite fringe projection profilometry for single-shot absolute 3D shape measurement. OEA. 5(5):210021–210021. doi: 10.29026/oea.2022.210021.
  • Li J, Wang Z, Wu X, Xu CY, Guo S, Chen X. 2020. Toward monitoring short-term droughts using a novel daily scale, standardized antecedent precipitation evapotranspiration index. J Hydrometeorol. 21(5):891–908. doi: 10.1175/JHM-D-19-0298.1.
  • Li J, Zhou N, Sun J, Zhou S, Bai Z, Lu L, Chen Q, Zuo C. 2022. Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy. Light Sci Appl. 11(1):2–14. doi: 10.1038/s41377-022-00815-7.
  • Luo J, Niu F, Lin Z, Liu M, Yin G, Gao Z. 2022. Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years. CATENA. 217:106497. doi: 10.1016/j.catena.2022.106497.
  • Magesh NS, Chandrasekar N, Soundranayagam JP. 2012. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci Front. 3(2):189–196. doi: 10.1016/j.gsf.2011.10.007.
  • Mandal U, Sahoo S, Munusamy SB, Dhar A, Panda SN, Kar A, Mishra PK. 2016. Delineation of groundwater potential zones of coastal groundwater basin using multi-criteria decision making technique. Water Resour Manage. 30(12):4293–4310. doi: 10.1007/S11269-016-1421-8/TABLES/3.
  • 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. doi: 10.3390/rs12172742.
  • Muavhi N, Humphrey K, Mulalo T, Mutoti I, 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. MutotiGeocarto Int. 37(21):6311–6330. doi: 10.1080/10106049.2021.1936212.
  • Muhammad S, Khalid P. 2017. Hydrogeophysical investigations for assessing the groundwater potential in part of the Peshawar basin. Pakistan. Environ Earth Sci. 76(14):1–12. doi: 10.1007/S12665-017-6833-0/FIGURES/8.
  • Mukherjee A, Sarkar S, Chakraborty M, Duttagupta S, Bhattacharya A, Saha D, Bhattacharya P, Mitra A, Gupta S. 2021. Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale AI-based modeling. Sci Total Environ. 759:143511. doi: 10.1016/j.scitotenv.2020.143511.
  • Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O. 2017. A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. J Hydrol. 548:471–483. doi: 10.1016/j.jhydrol.2017.03.020.
  • Naghibi SA, Pourghasemi HR. 2015. A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manage. 29(14):5217–5236. doi: 10.1007/s11269-015-1114-8.
  • 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. doi: 10.1007/s00704-016-2022-4.
  • Naghibi SA, Pourghasemi HR, Dixon B. 2016. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess. 188(1):44. doi: 10.1007/s10661-015-5049-6.
  • Naghibi SA, Pourghasemi HR, Pourtaghi ZS, Rezaei A. 2015. Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran. Earth Sci Inform. 8(1):171–186. doi: 10.1007/s12145-014-0145-7.
  • Nazir-Ur-Rehman Ahmad S, Faisal S, Ali F, Ullah S, Ullah R, Khan MA, Afrasiab, Waqar Azeem M. 2020. Assessment of drinking water quality and human health risks in karak and adjoining areas, Southeastern Kohat Basin, Pakistan. J Himal Earth Sci. 53(1):126–139.
  • Nguyen PT, Ha DH, Jaafari A, Nguyen HD, Van Phong T, Al-Ansari N, Prakash I, Le HV, Pham BT. 2020. Groundwater potential mapping combining artificial neural network and real adaboost ensemble technique: the DakNong province case-study, Vietnam. Int J Environ Res Public Health. 17(7):2473. doi: 10.3390/ijerph17072473.
  • 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. J Hydrol. 399(3-4):158–172. doi: 10.1016/j.jhydrol.2010.12.027.
  • Ozdemir A. 2011. GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J Hydrol. 411(3-4):290–308. doi: 10.1016/j.jhydrol.2011.10.010.
  • Pande CB, Moharir KN, Panneerselvam B, Singh SK, Elbeltagi A, Pham QB, Varade AM, Rajesh J. 2021. Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques. Appl Water Sci. 11(12):1–20. doi: 10.1007/s13201-021-01522-1.
  • Pham BT, Jaafari A, Phong TV, Mafi-Gholami D, Amiri M, Van Tao N, Duong V-H, Prakash I. 2021. Naïve Bayes ensemble models for groundwater potential mapping. Ecol Inform. 64:101389. doi: 10.1016/j.ecoinf.2021.101389.
  • Pourtaghi ZS, Pourghasemi HR. 2014. GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province, Iran. Hydrogeol J. 22(3):643–662. doi: 10.1007/s10040-013-1089-6.
  • Prasad P, Loveson VJ, Kotha M, Yadav R. 2020. Application of machine learning techniques in groundwater potential mapping along the west coast of India. GISci Remote Sens. 57(6):735–752. doi: 10.1080/15481603.2020.1794104.
  • Prasad RK, Mondal NC, Banerjee P, Nandakumar MV, Singh VS. 2008. Deciphering potential groundwater zone in hard rock through the application of GIS. Environ Geol. 55(3):467–475. doi: 10.1007/S00254-007-0992-3/FIGURES/9.
  • Qiu D, Zhu G, Bhat MA, Wang L, Liu Y, Sang L, Lin X, Zhang W, Sun N. 2023. Water use strategy of nitraria tangutorum shrubs in ecological water delivery area of the lower inland river: based on stable isotope data. J Hydrol. 624:129918. doi: 10.1016/j.jhydrol.2023.129918.
  • Rahmati O, Nazari Samani A, Mahdavi M, Pourghasemi HR, Zeinivand H. 2015. Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab J Geosci. 8(9):7059–7071. doi: 10.1007/s12517-014-1668-4.
  • Rahmati O, Pourghasemi HR, Melesse AM. 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. doi: 10.1016/j.catena.2015.10.010.
  • Regmi NR, Giardino JR, McDonald EV, Vitek JD. 2015. A Review of mass movement processes and risk in the critical zone of earth. In: Dev Earth Surf Process. Vol. 19 [place unknown]; p. 319–362. doi: 10.1016/B978-0-444-63369-9.00011-2.
  • Rehman G, Ahmad S, Ali F, Zahid M, Journal P. 2009. Structural Geology of the Shakar Khel Area, Karak District, Khyber Pakhtunkhwa, Pakistan. Pakistan J Hydrocarb Res. 19:11–17.
  • Rui S, Zhou Z, Jostad HP, Wang L, Guo Z. 2023. Numerical prediction of potential 3-dimensional seabed trench profiles considering complex motions of mooring line. Appl Ocean Res. 139:103704. doi: 10.1016/j.apor.2023.103704.
  • Sarkar S, Roy AK, Martha TR. 2013. Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. J Geol Soc India. 82(4):351–362. doi: 10.1007/s12594-013-0162-z.
  • Sarkar D, Saha S, Mondal P. 2022. GIS-based frequency ratio and Shannon’s entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India. Int J Environ Sci Technol. 19(9):8911–8932. doi: 10.1007/s13762-021-03627-1.
  • Sutradhar S, Sarkar D, Bhuimali A, Mondal P. 2022. Integration of different geospatial factors to delineate groundwater potential zones using multi-influencing factors under remote sensing and GIS environment: a study on Dakshin Dinajpur district, West Bengal, India. Sustain Water Resour Manag. 8(1):37. doi: 10.1007/s40899-022-00630-3.
  • Tahmassebipoor N, Rahmati O, Noormohamadi F, Lee S. 2016. Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci. 9(1):1–18. doi: 10.1007/s12517-015-2166-z.
  • Tariq A, Hashemi Beni L, Ali S, Adnan S, Hatamleh WA. 2023. An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge. Groundw Sustain Dev. 23(8):100998. doi: 10.1016/j.gsd.2023.100998.
  • 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. doi: 10.1007/s10661-022-10738-w.
  • Tian H, Huang N, Niu Z, Qin Y, Pei J, Wang J. 2019. Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote Sens. 11(7):820. doi: 10.3390/rs11070820.
  • Tian H, Pei J, Huang J, Li X, Wang J, Zhou B, Qin Y, Wang L. 2020. Garlic and winter wheat identification based on active and passive satellite imagery and the Google earth engine in Northern China. Remote Sens. 12(21):3539. doi: 10.3390/rs12213539.
  • Venkatesan G, Pitchaikani S, Saravanan S. 2019. Assessment of groundwater vulnerability using GIS and DRASTIC for Upper Palar River Basin, Tamil Nadu. J Geol Soc India. 94(4):387–394. doi: 10.1007/s12594-019-1326-2.
  • Wubalem A, Meten M. 2020. Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci. 2(5):1–19. doi: 10.1007/S42452-020-2563-0/TABLES/6.
  • Xu Z, Li X, Li J, Xue Y, Jiang S, Liu L, Luo Q, Wu K, Zhang N, Feng Y, et al. 2022. Characteristics of source rocks and genetic origins of natural gas in deep formations, Gudian depression, Songliao Basin, NE China. ACS Earth Space Chem. 6(7):1750–1771. doi: 10.1021/acsearthspacechem.2c00065.
  • Yang M, Wang H, Hu K, Yin G, Wei Z. 2022. IA-Net: an inception–attention-module-based network for classifying underwater images from others. IEEE J Oceanic Eng. 47(3):704–717. doi: 10.1109/JOE.2021.3126090.
  • Yawar Ali Khan M, ElKashouty M. 2023. Watershed prioritization and hydro-morphometric analysis for the potential development of Tabuk Basin, Saudi Arabia using multivariate statistical analysis and coupled RS-GIS approach. Ecol Indic. 154:110766. doi: 10.1016/j.ecolind.2023.110766.
  • Yesilnacar EK. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey.
  • Yin L, Wang L, Li J, Lu S, Tian J, Yin Z, Liu S, Zheng W. 2023. YOLOV4_CSPBi: enhanced land target detection model. Land. 12(9):1813. doi: 10.3390/land12091813.
  • Yin L, Wang L, Li T, Lu S, Yin Z, Liu X, Li X, Zheng W. 2023. U-Net-STN: a novel end-to-end lake boundary prediction model. Land. 12(8):1602. doi: 10.3390/land12081602.
  • Yin KL, Yan TZ. 1988. Statistical prediction models for slope instability of metamorphosed rocks. Landslides Proc 5th Symp Lausanne, 1988, Vol 2. p. 1269–1272. doi: 10.1016/0148-9062(90)90358-9.
  • Zabihi M, Pourghasemi HR, Pourtaghi ZS, Behzadfar M. 2016. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ Earth Sci. 75(8):2–19. doi: 10.1007/s12665-016-5424-9.
  • Zahir M, Ahmedl Z, Ansari MT, Shakir U, Subhan M. 2020. Hydrogeophysical investigation for groundwater potential through electrical resistivity survey in Islamabad, Pakistan. J Geogr Soc Sci. 2(2):147–163. http://jgssjournal.uob.edu.pk/journal/index.php/jgss/article/view/16.
  • Zhou G, Deng R, Zhou X, Long S, Li W, Lin G, Li X. 2022. Gaussian Inflection point selection for LiDAR hidden echo signal decomposition. IEEE Geosci Remote Sens Lett. 19:1–5. doi: 10.1109/LGRS.2021.3107438.
  • Zhou G, Liu X. 2022. Orthorectification model for extra-length linear array imagery. IEEE Trans Geosci Remote Sens. 60:1–10. doi: 10.1109/TGRS.2022.3223911.
  • Zhou G, Li W, Zhou X, Tan Y, Lin G, Li X, Deng R. 2021. An innovative echo detection system with STM32 gated and PMT adjustable gain for airborne LiDAR. Int J Remote Sens. 42(24):9187–9211. doi: 10.1080/01431161.2021.1975844.
  • Zhu G, Liu Y, Shi P, Jia W, Zhou J, Liu Y, Ma X, Pan H, Zhang Y, Zhang Z, et al. 2022. Stable water isotope monitoring network of different water bodies in Shiyang River basin, a typical arid river in China. Earth Syst Sci Data. 14(8):3773–3789. doi: 10.5194/essd-14-3773-2022.
  • Zhuo Z, Du L, Lu X, Chen J, Cao Z. 2022. Smoothed Lv distribution based three-dimensional imaging for spinning space debris. IEEE Trans Geosci Remote Sens. 60:1–13. doi: 10.1109/TGRS.2022.3174677.