Abstract
The main problem faced by the developing countries globally in land management is soil erosion. This study maps an area of soil erosion prone in the Bone Watershed, Province Gorontalo, Indonesia, using topographical attributes sourced from a Digital Elevation Model with a spatial resolution of 30 m. The geographic information system methods are integrated with the analytic hierarchy process. This study considered ten topographic factors: elevation, slope, profile curvature, plan curvature, aspect, flow length to the nearest stream, stream power index, topographic wetness index, terrain ruggedness index, and sediment transport index. The model’s performance is assessed using the area under curve-receiver operating characteristic (AUC ROC) approach. According to the findings, slope and sediment transport index, as well as flow length to the nearest stream, had the most significant impact on soil erosion prone, with values of 0.202, 0.190, and 0.126, respectively. The overlaying maps of all topographic factors were divided into five zones: very low, low, moderate, high, and very high. High and very high zones were discovered in 22.7% and 0.3% of the entire research area, indicating that the area is at a severe rate of soil erosion. The AUC ROC value for validating mapping results with datasets is 75.4%, showing that the AHP-GIS model’s prediction is fair classification in mapping soil erosion-prone zones in the Bone Watershed. This research identifies significant soil erosion-prone zones in the hopes of assisting stakeholders and planners in developing a plan to reduce land degradation.







Similar content being viewed by others
References
Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci. https://doi.org/10.1007/s12517-017-2980-6
Ajin RS, Krishnamurthy RR, Jayaprakash M, Vinod PG (2013) Flood hazard assessment of Vamanapuram River Basin, Kerala, India: an approach using Remote Sensing & GIS techniques. Pelagia Res Library Adv 4(3):263–274
Al-Bawi AJ, Al-Abadi AM, Pradhan B, Alamri AM (2021) Assessing gully erosion susceptibility using topographic derived attributes, multi-criteria decision-making, and machine learning classifiers. Geomat Nat Haz Risk 12(1):3035–3062. https://doi.org/10.1080/19475705.2021.1994024
Arabameri A, Pradhan B, Pourghasemi HR, Rezaei K (2018a) Identification of erosion-prone areas using different multi-criteria decision-making techniques and gis. Geomat Nat Haz Risk 9(1):1129–1155. https://doi.org/10.1080/19475705.2018.1513084
Arabameri A, Rezaei K, Pourghasemi HR, Lee S, Yamani M (2018b) GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique. Environ Earth Sci. https://doi.org/10.1007/s12665-018-7808-5
Arabameri A, Cerda A, Tiefenbacher JP (2019) Spatial pattern analysis and prediction of gully erosion using novel hybrid model of entropy-weight of evidence. Water (Switzerland) 11(6):1–23. https://doi.org/10.3390/w11061129
Arabameri A, Tiefenbacher JP, Blaschke T, Pradhan B, Bui DT (2020) Morphometric analysis for soil erosion susceptibility mapping using novel gis-based ensemble model. Remote Sens 12(5):1–24. https://doi.org/10.3390/rs12050874
Ashiagbor G, Forkuo E, Laari P, Aabeyir R (2012) Modeling soil erosion using rusle and Gis tools. Int J Remote Sens Geosci 2:7–17
Aslam B, Maqsoom A, Alaloul SW, Musarat AM, Jabbar T, Zafar A (2021) Soil erosion susceptibility mapping using a GIS-based multi-criteria decision approach: case of district Chitral, Pakistan. Ain Shams Eng J 12(2):1637–1649. https://doi.org/10.1016/j.asej.2020.09.015
Bhattacharya RK, Chatterjee ND, Das K (2020) Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: a comparison between MCDM and SWAT models. Sci Total Environ J 734:1–21. https://doi.org/10.1016/j.scitotenv.2020.139474. (0048-9697/©)
Biswas H, Raizada A, Mandal D, Kumar S, Srinivas S, Mishra PK (2015) Identification of areas vulnerable to soil erosion risk in India using GIS methods. Solid Earth 6(4):1247–1257. https://doi.org/10.5194/se-6-1247-2015
Chuma GB, Bora FS, Ndeko AB, Mugumaarhahama Y, Cirezi NC, Mondo JM, Bagula EM, Karume K, Mushagalusa GN, Schimtz S (2021) Estimation of soil erosion using RUSLE modeling and geospatial tools in a tea production watershed (Chisheke in Walungu), eastern Democratic Republic of Congo. Model Earth Syst Environ. https://doi.org/10.1007/s40808-021-01134-3
Comino RJ, Iserloh T, Lassu T, Cerdà A, Keesstra SD, Prosdocimi M, Brings C, Marzen M, Ramos MC, Senciales JM, Sinoga RJD, Seeger M, Ries JB (2016) Quantitative comparison of initial soil erosion processes and runoff generation in Spanish and German vineyards. Sci Total Environ 565:1165–1174. https://doi.org/10.1016/j.scitotenv.2016.05.163
Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Märker M (2014) Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology 204:399–411. https://doi.org/10.1016/j.geomorph.2013.08.021
Das B, Bordoloi R, Thungon LT, Paul A, Pandey PK, Mishra M, Tripathi OP (2020) An integrated approach of GIS, RUSLE and AHP to model soil erosion in West Kameng watershed, Arunachal Pradesh. J Earth Syst Sci 129(1):1–18. https://doi.org/10.1007/s12040-020-1356-6
Debelo G, Tadele K, Koriche SA (2017) Morphometric analysis to identify erosion prone areas on the Upper Blue Nile Using Gis (Case Study of Didessa and Jema Sub-Basin, Ethiopia). Int Res J Eng Technol 4(8):1773–1784. www.irjet.net
Ejegu MA, Yegizaw ES (2021) Modeling soil erosion susceptibility and LULC dynamics for land degradation management using geoinformation technology in Debre Tabor district, Northwestern highlands of Ethiopia. J Degrade Min Land Manage 8(2):2623–2633. https://doi.org/10.15243/jdmlm.2021.082.2623
Ekrami M, Marj AF, Barkhordari J, Dashtakian K (2016) Drought vulnerability mapping using AHP method in arid and semiarid areas: a case study for Taft Township, Yazd Province, Iran. Environ Earth Sci. https://doi.org/10.1007/s12665-016-5822-z
Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai S (2019) Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Total Environ 668:124–138. https://doi.org/10.1016/j.scitotenv.2019.02.436
Georgiou D, Mohammed ES, Rozakis S (2015) Multi-criteria decision making on the energy supply configuration of autonomous desalination units. Renewab Energy 75:459–467. https://doi.org/10.1016/j.renene.2014.09.036
Getnet T, Mulu A (2021) Assessment of soil erosion rate and hotspot areas using RUSLE and multi-criteria evaluation technique at Jedeb watershed, Upper Blue Nile, Amhara Region, Ethiopia. Environ Challenges 4:100174. https://doi.org/10.1016/j.envc.2021.100174
Gideon D, Mustafa FB, Victor I (2021) The application of an expert knowledge-driven approach for assessing gully erosion susceptibility in the subtropical Nigerian savannah. Singap J Trop Geogr 42(1):107–131. https://doi.org/10.1111/sjtg.12348
Golestani G, Issazadeh L, Serajamani R (2014) Lithology effects on gully erosion in Ghoori chay Watershed using RS & GIS. Int J Biosci 4(2):71–76. https://doi.org/10.1269/ijb/4.2.71-76
Gómez-Gutiérrez Á, Conoscenti C, Angileri SE, Rotigliano E, Schnabel S (2015) Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations. Nat Hazards 79:291–314. https://doi.org/10.1007/s11069-015-1703-0
Halefom A, Teshome A (2019) Modelling and mapping of erosion potentiality watersheds using AHP and GIS technique: a case study of Alamata Watershed, South Tigray, Ethiopia. Modeling Earth Syst Environ 5(3):819–831. https://doi.org/10.1007/s40808-018-00568-6
Hembram TK, Saha S (2020) Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand, Eastern India. Environ Dev Sustain 22(2):1241–1268. https://doi.org/10.1007/s10668-018-0247-3
Igwe O, John UI, Solomon O, Obinna O (2020) GIS-based gully erosion susceptibility modeling, adapting bivariate statistical method and AHP approach in Gombe town and environs Northeast Nigeria. Geoenviron Disasters. https://doi.org/10.1186/s40677-020-00166-8
Jiang C, Fan W, Yu N, Liu E (2021) Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2021.147040
Jothimani M, Getahun E, Abebe A (2022) Remote sensing, GIS, and RUSLE in soil loss estimation in the Kulfo river catchment, Rift valley, Southern Ethiopia. J Degraded Mining Lands Manag 9(2):3307–3315. https://doi.org/10.15243/jdmlm.2022.092.3307
Kabo-bah KJ, Guoan T, Yang X, Na J, Xiong L (2021) Erosion potential mapping using analytical hierarchy process (AHP) and fractal dimension. Heliyon 7(6):e07125. https://doi.org/10.1016/j.heliyon.2021.e07125
Kumar R, Anbalagan R (2016) Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286. https://doi.org/10.1007/s12594-016-0395-8
Kusairi A, Yulia R (2020) Mapping of dengue fever distribution based on indonesian national standard cartography rules as an prevention indicator of outbreaks. Jurnal Pendidikan IPA Indonesia 9(1):91–96. https://doi.org/10.15294/jpii.v9i1.21811
Lucà F, Conforti M, Robustelli G (2011) Comparison of GIS-based gullying susceptibility mapping using bivariate and multivariate statistics: Northern Calabria, South Italy. Geomorphology 134:297–308. https://doi.org/10.1016/j.geomorph.2011.07.006
Mahala A (2018) Soil erosion estimation using RUSLE and GIS techniques—a study of a plateau fringe region of tropical environment. Arab J Geosci. https://doi.org/10.1007/s12517-018-3703-3
Marondedze AK, Schütt B (2020) Assessment of soil erosion using the rusle model for the Epworth district of the Harare metropolitan province, Zimbabwe. Sustainability (Switzerland) 12(20):1–24. https://doi.org/10.3390/su12208531
Masselink RJH, Heckmann T, Temme AJAM, Anders NS, Gooren HPA, Keesstra SD (2017) A network theory approach for a better understanding of overland flow connectivity. Hydrol Process 31(1):207–220. https://doi.org/10.1002/hyp.10993
Mokarram M, Zarei AR (2021) Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions. Geoderma 403:115379. https://doi.org/10.1016/j.geoderma.2021.115379
Molla T, Sisheber B (2017) Estimating soil erosion risk and evaluating erosion control measures for soil conservation planning at Koga watershed in the highlands of Ethiopia. Solid Earth 8(1):13–25. https://doi.org/10.5194/se-8-13-2017
Mujib MA, Apriyanto B, Kurnianto FA, Ikhsan A, Nurdin EA, Pangastuti EI, Astutik S (2021) Assessment of Flood Hazard Mapping Based on Analytical Hierarchy Process (AHP) and GIS: Application in Kencong District, Jember Regency. Indonesia Geosfera Indonesia 6(3):353–376
Mulliner E, Malys N, Maliene V (2016) Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega (United Kingdom) 59:146–156. https://doi.org/10.1016/j.omega.2015.05.013
Nitheshnirmal S, Bhardwaj A, Dineshkumar C, Rahaman SA (2019) Prioritization of erosion prone micro-watersheds using morphometric analysis coupled with multi-criteria decision making. Proceedings 24(1):11. https://doi.org/10.3390/iecg2019-06207
Olii MR, Ichsan I (2020) Assessment of critical land using geographic information systems—a case study of Limboto watershed, Gorontalo. IOP Conference Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/437/1/012053
Olii MR, Olii A, Pakaya R (2021a) Analysis of spatial distribution of the drought hazard index (DHI) by integration AHP-GIS-remote sensing in Gorontalo regency. J Civil Eng Forum 8(1):81–96. https://doi.org/10.2214/jcef.3595
Olii MR, Olii A, Pakaya R (2021b) The integrated spatial assessment of the flood hazard using AHP-GIS: the case study of Gorontalo regency. Indonesian J Geogr 53(1):126–135. https://doi.org/10.22146/ijg.59999
Palchaudhuri M, Biswas S (2016) Application of AHP with GIS in drought risk assessment for Puruliya district. India Nat Hazards 84(3):1905–1920. https://doi.org/10.1007/s11069-016-2526-3
Prasad AS, Pandey BW, Leimgruber W, Kunwar RM (2016) Mountain hazard susceptibility and livelihood security in the upper catchment area of the river Beas, Kullu Valley, Himachal Pradesh, India. Geoenviron Disasters. https://doi.org/10.1186/s40677-016-0037-x
Prasannakumar V, Shiny R, Geetha N, Vijith H (2011) Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala India. Environ Earth Sci 64(4):965–972. https://doi.org/10.1007/s12665-011-0913-3
Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017) Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: an integrated framework. Sci Total Environ 579:913–927. https://doi.org/10.1016/j.scitotenv.2016.10.176
Rahmati O, Kalantari Z, Samadi M, Uuemaa E, Moghaddam DD, Nalivan OA, Destouni G, Bui DT (2019) GIS-based site selection for check dams in watersheds: considering geomorphometric and topo-hydrological factors. Sustainability (Switzerland). https://doi.org/10.3390/su11205639
Saaty TL (1980) The analytic hierarchy process. McGraw Hill. International
Saaty TL (2002) Decision making with the analytic hierarchy process. Scientia Iranica 9(3):215–229. https://doi.org/10.1504/ijssci.2008.017590
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Services Sci 1(1):83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Saha S (2017) Groundwater potential mapping using analytical hierarchical process: a study on Md. Bazar Block of Birbhum District, West Bengal. Spatial Inform Res 25(4):615–626. https://doi.org/10.1007/s41324-017-0127-1
Shit PK, Nandi AS, Bhunia GS (2015) Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India. Model Earth Syst Environ 1(3):1–12. https://doi.org/10.1007/s40808-015-0032-3
Tehrany MS, Shabani F, Javier DN, Kumar L (2017) Soil erosion susceptibility mapping for current and 2100 climate conditions using evidential belief function and frequency ratio. Geomat Nat Haz Risk 8(2):1695–1714. https://doi.org/10.1080/19475705.2017.1384406
Valipour M, Mohseni N, Hosseinzadeh SR (2022) Factors affecting topographic thresholds in gully erosion occurrence and its management using predictive machine learning models. Earth Sci Res J 25(4):423–432. https://doi.org/10.15446/esrj.v25n4.95748
Vulević T, Dragović N, Kostadinov S, Simić SB, Milovanović I (2015) Prioritization of soil erosion vulnerable areas using multi-criteria analysis methods. Polish J Environ Stud 24(1):317–323. https://doi.org/10.1524/pjoes/28962
Zabihi M, Mirchooli F, Motevalli A, Darvishan AK, Pourghasemi HR, Zakeri MA, Sadighi F (2018) Spatial modelling of gully erosion in Mazandaran Province, northern Iran. CATENA 161:1–13. https://doi.org/10.1016/j.catena.2017.10.010
Zakerinejad R, Maerker M (2015) An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Nat Hazards 79:25–50. https://doi.org/10.1007/s11069-015-1700-3
Acknowledgements
The authors would like to thank the Department of Civil Engineering, Faculty of Engineering, Universitas Gorontalo for the partial financial support through an internal faculty grant. We also want to thank the anonymous reviewers for their constructive comments and express their gratitude to everyone who helped make this study a reality. The authors would also like to thank all the free database and satellite data providers whose data was downloaded from their web portal and used in this study.
Funding
The authors would like to thank the Department of Civil Engineering, Faculty of Engineering, Universitas Gorontalo for the partial financial support through an internal faculty grant.
Author information
Authors and Affiliations
Contributions
MRO designed the model, and the computational framework and wrote the manuscript. AO analyzed the data and performed the calculations. RP drafted and designed the figures. MYUPO verified the analytical methods. All authors discussed the results and contributed to the final manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could appear to have influenced the work described in this paper.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Olii, M.R., Olii, A., Pakaya, R. et al. GIS-based analytic hierarchy process (AHP) for soil erosion-prone areas mapping in the Bone Watershed, Gorontalo, Indonesia. Environ Earth Sci 82, 225 (2023). https://doi.org/10.1007/s12665-023-10913-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-023-10913-3