Abstract
Soil erosion is a pressing natural phenomenon confronting nations all over the world. The study's objectives are to establish an evaluation model of soil erosion in the Paguyaman Watershed, Gorontalo, Indonesia, using the Analytic Hierarchy Process (AHP). Eight different factors, slope, elevation, slope length, annual rainfall, average wind speed clay ratio, NDVI, and NDMI were considered in this study. Each factor has been assigned a weight, and maps have been created using a Geographic Information System and remote sensing tools. The combined map of all maps indicates the intensity of soil erosion in five separate classes: very high (0.07%), high (18.90%), moderate (46.69%), low (5.94%), and very low (0%). The high and moderate class is the dominant study area, which shows that the area is at high risk of soil erosion. Slope (0.24), NDVI (0.23), and annual rainfall (0.15) were found to be the dominant factors influencing the soil erosion risk. According to the AUC ROC value of 0.762, the soil erosion risk map has an overall success rate of 76.2%. The findings of this study may be used by policymakers to adopt suitable conservation programs to prevent soil erosion or to advocate soil conservation acts.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12517-024-12032-0/MediaObjects/12517_2024_12032_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12517-024-12032-0/MediaObjects/12517_2024_12032_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12517-024-12032-0/MediaObjects/12517_2024_12032_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12517-024-12032-0/MediaObjects/12517_2024_12032_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs12517-024-12032-0/MediaObjects/12517_2024_12032_Fig5_HTML.png)
Similar content being viewed by others
Data availability
The authors confirm that the data supporting the findings of this study are available within the article.
References
Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility map** using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arab J Geosci 10:8. https://doi.org/10.1007/s12517-017-2980-6
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
Allafta H, Opp C (2022) Soil erosion assessment using the RUSLE model, remote sensing, and GIS in the Shatt Al-Arab Basin (Iraq-Iran). Appl Sci (Switzerland) 12(15):1–17. https://doi.org/10.3390/app12157776
Almouctar MAS, Wu Y, Zhao F, Dossou JF (2021) Soil erosion assessment using the rusle model and geospatial techniques (Remote sensing and gis) in south-central niger (maradi region). Water (Switzerland) 13:24. https://doi.org/10.3390/w13243511
Anteneh M (2021) Spatial estimation of soil erosion using RUSLE modeling: the case of kaffa zone, South western Ethiopia. Res Square 1–13. https://doi.org/10.21203/rs.3.rs-753720/v1
Arabameri A, Rezaei K, Pourghasemi HR, Lee S, Yamani M (2018) GIS-based gully erosion susceptibility map**: a comparison among three data-driven models and AHP knowledge-based technique. Environ Earth Sci 77:17. 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
Aslam B, Maqsoom A, Alaloul SW, Musarat AM, Jabbar T, Zafar A (2021) Soil erosion susceptibility map** 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
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
Borrelli P, Robinson DA, Fleischer LR, Lugato E, Ballabio C, Alewell C, Meusburger K, Modugno S, Schütt B, Ferro V, Bagarello V, Oost KV, Montanarella L, Panagos P (2017) An assessment of the global impact of 21st century land use change on soil erosion. Nat Commun 8:1. https://doi.org/10.1038/s41467-017-02142-7
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
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
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
Farhan Y, Zregat D, Farhan I (2013) Spatial estimation of soil erosion risk using RUSLE approach, RS, and GIS Techniques: A Case Study of Kufranja Watershed, Northern Jordan. J Water Resour Prot 05(12):1247–1261. https://doi.org/10.4236/jwarp.2013.512134
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. Renew 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 4:1–11. https://doi.org/10.1016/j.envc.2021.100174
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 map** of erosion potentiality watersheds using AHP and GIS technique: a case study of Alamata Watershed, South Tigray. Ethiopia Model Earth Syst Environ 5(3):819–831. https://doi.org/10.1007/s40808-018-00568-6
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 7:1. https://doi.org/10.1186/s40677-020-00166-8
Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11(4):909–926. https://doi.org/10.1007/s13762-013-0464-0
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 783:1–13. 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 Min Lands Manag 9(2):3307–3315. https://doi.org/10.15243/jdmlm.2022.092.3307
Kabo-bah KJ, Guoan T, Yang X, Na J, ** using analytical hierarchy process (AHP) and fractal dimension. Heliyon 7:6. https://doi.org/10.1016/j.heliyon.2021.e07125
Keesstra SD, Bouma J, Wallinga J, Tittonell P, Smith P, Cerdà A, Montanarella L, Quinton JN, Pachepsky Y, Van Der Putten WH, Bardgett RD, Moolenaar S, Mol G, Jansen B, Fresco LO (2016) The significance of soils and soil science towards realization of the United Nations sustainable development goals. Soil 2(2):111–128. https://doi.org/10.5194/soil-2-111-2016
Kil SH, Lee DK, Kim JH, Li MH, Newman G (2016) Utilizing the analytic hierarchy process to establish weighted values for evaluating the stability of slope revegetation based on hydroseeding applications in South Korea. Sustainability (Switzerland) 8(1):1–17. https://doi.org/10.3390/su8010058
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
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 403:1–16. https://doi.org/10.1016/j.geoderma.2021.115379
Mokarrama M, Hojati M (2018) Landform classification using a sub-pixel spatial attraction model to increase spatial resolution of digital elevation model (DEM). Egypt J Remote Sens Space Sci 21(1):111–120. https://doi.org/10.1016/j.ejrs.2016.11.005
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 map** based on analytical hierarchy process (AHP) and GIS: application in Kencong District, Jember Regency Indonesia. Geos Ind 6(3):353–376
Ochoa PA, Fries A, Mejía D, Burneo JI, Ruíz-Sinoga JD, Cerdà A (2016) Effects of climate, land cover and topography on soil erosion risk in a semiarid basin of the Andes. CATENA 140:31–42. https://doi.org/10.1016/j.catena.2016.01.011
Olii MR, Ichsan I (2020) Assessment of critical land using geographic information systems - a case study of Limboto watershed, Gorontalo. IOP Conf Ser: Earth Environ Sci 437(1):1–9. https://doi.org/10.1088/1755-1315/437/1/012053
Olii MR, Olii A, Pakaya R, Olii MYUP (2023) GIS-based analytic hierarchy process (AHP) for soil erosion-prone areas map** in the Bone Watershed, Gorontalo. Ind Environ Earth Sci 82(9):1–14. https://doi.org/10.1007/s12665-023-10913-3
Olii MR, Kironoto BA, Olii A, Pakaya R, Olii AKZ (2024) Advancing soil erosion assessment: application of remote sensing and geospatial techniques in Bulango Ulu Reservoir Basin. E3S Web Conf 476:1–15. https://doi.org/10.1051/e3sconf/202447601041
Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2013) A comparative assessment of prediction capabilities of Dempster-Shafer and Weights-of-evidence models in landslide susceptibility map** using GIS. Geomat Nat Haz Risk 4(2):93–118. https://doi.org/10.1080/19475705.2012.662915
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
Rahaman SA, Ajeez SA, Aruchamy S, Jegankumar R (2015) Prioritization of sub watershed based on morphometric characteristics using fuzzy analytical hierarchy process and geographical information system – a study of Kallar Watershed, Tamil Nadu. Aquat Procedia 4:1322–1330. https://doi.org/10.1016/j.aqpro.2015.02.172
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
Saaty TL (2004) Decision making — the Analytic Hierarchy and Network Processes (AHP/ANP). J Syst Sci Syst Eng 13(1):1–35. https://doi.org/10.1007/s11518-006-0151-5
Salhi A, El Hasnaoui Y, Pérez Cutillas P, Heggy E (2023) Soil erosion and hydroclimatic hazards in major African port cities: the case study of Tangier. Sci Rep 13:1. https://doi.org/10.1038/s41598-023-40135-3
Senanayake S, Pradhan B, Huete A, Brennan J (2020) A review on assessing and map** soil erosion hazard using geo-informatics technology for farming system management. Remote Sensing 12(24):1–25. https://doi.org/10.3390/rs12244063
Siswanto SY, Sule MIS (2019) The Impact of slope steepness and land use type on soil properties in Cirandu Sub-Sub Catchment, Citarum Watershed. IOP Conf Ser: Earth Environ Sci 393:1. https://doi.org/10.1088/1755-1315/393/1/012059
Sujatha ER, Sridhar V (2018) Spatial prediction of erosion risk of a small mountainous watershed using RUSLE: a case-study of the palar sub-watershed in Kodaikanal. South India Water (switzerland) 10(11):1–17. https://doi.org/10.3390/w10111608
Tehrany MS, Shabani F, Javier DN, Kumar L (2017) Soil erosion susceptibility map** 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
Thapa P (2020) Spatial estimation of soil erosion using RUSLE modeling: a case study of Dolakha district Nepal. Environ Syst Res 9:1. https://doi.org/10.1186/s40068-020-00177-2
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. Pol J Environ Stud 24(1):317–323. https://doi.org/10.15244/pjoes/28962
Yang Q, **e Y, Li W, Jiang Z, Li H, Qin X (2014) Assessing soil erosion risk in karst area using fuzzy modeling and method of the analytical hierarchy process. Environ Earth Sci 71(1):287–292. https://doi.org/10.1007/s12665-013-2432-x
Yariyan P, Avand M, Abbaspour RA, Haghighi TA, Costache R, Ghorbanzadeh O, Janizadeh S, Blaschke T (2020) Flood susceptibility map** using an improved analytic network process with statistical models flood susceptibility map** using an improved analytic network process with statistical models. Geomat Nat Haz Risk 11(1):2282–2314. https://doi.org/10.1080/19475705.2020.1836036
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
Zhou P, Ge Y, Jiang Y, **e Y, Si Z, Yang H, Huo H-Y, Wei G, Yu J (2020) Assessment of soil erosion by the RUSLE model using remote sensing and GIS: a case study of Jilin Province of China. Preprints. https://doi.org/10.20944/preprints202011.0435.v1
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 are also would like 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 of the free database and satellite data providers whose data was downloaded from their web portal and used in this study.
Author information
Authors and Affiliations
Contributions
Olii, M.R. designed the model and the computational framework and wrote the manuscript. Olii, A. and Kironoto, B.A. analyzed the data and performed the calculations. Pakaya, R. drafted and designed the figures. Olii, A.K.Z. verified the analytical methods. All authors discussed the results and contributed to the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no competing interests.
Additional information
Responsible Editor: Stefan Grab
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.K.Z., Olii, A. et al. Spatial modeling of soil erosion risk: a multi-criteria decision-making (MCDM) approach in the paguyaman watershed, gorontalo, Indonesia. Arab J Geosci 17, 226 (2024). https://doi.org/10.1007/s12517-024-12032-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12517-024-12032-0