Abstract

Understanding susceptibility is crucial for effective risk assessment, mitigation, and management. This chapter delves into the applied methods and techniques for susceptibility modeling and map**. It also describes the obtained findings by showcasing instances of susceptibility maps produced by the applied methods in parallel with their interpretation and significance. The chapter also shows the validation techniques and results in relation to the accuracy of the applied methods. Finally, we discuss the uncertainty and limitations involved in the study.

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References

  • Abul Hasanat MH et al (2010) Bayesian belief network learning algorithms for modeling contextual relationships in natural imagery: a comparative study. Artif Intell Rev 34(4):291–308

    Article  Google Scholar 

  • Ahmed B, Dewan A (2017) Application of bivariate and multivariate statistical techniques in landslide susceptibility modeling in Chittagong City Corporation, Bangladesh. Remote Sens 9(4):304

    Article  Google Scholar 

  • Akgun A et al (2008) Landslide susceptibility map** for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6):1127–1143

    Article  Google Scholar 

  • Arabameri A et al (2020) An ensemble model for landslide susceptibility map** in a forested area. Geocarto Int 35(15):1680–1705

    Article  Google Scholar 

  • Asumadu-Sarkodie, S., et al. (2017). "Situational analysis of flood and drought in Rwanda."

    Google Scholar 

  • Bai S et al (2012) Combined landslide susceptibility map** after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena 99:18–25

    Article  Google Scholar 

  • Berkson J (1944) Application of the logistic function to bio-assay. J Am Stat Assoc 39(227):357–365

    Google Scholar 

  • Beydoun M, Guldmann J-M (2006) Vehicle characteristics and emissions: logit and regression analyses of I/M data from Massachusetts, Maryland, and Illinois. Transp Res Part D: Transp Environ 11(1):59–76

    Article  Google Scholar 

  • Bizimana H, Sönmez O (2015) Landslide occurrences in the hilly areas of Rwanda, their causes and protection measures. Disaster Science and Engineering 1(1):1–7

    Google Scholar 

  • Bizimana JP, Schilling M (2009) Geo-information Technology for Infrastructural Flood Risk Analysis in unplanned settlements: a case study of informal settlement flood risk in the Nyabugogo flood plain, Kigali City, Rwanda. Geospatial techniques in urban hazard and disaster analysis, Springer: 99-124

    Google Scholar 

  • Broeckx J et al (2018) A data-based landslide susceptibility map of Africa. Earth Sci Rev 185:102–121

    Article  Google Scholar 

  • Bui DT et al (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038–1054

    Article  Google Scholar 

  • Capitani M et al (2013) The slope aspect: a predisposing factor for landsliding? Compt Rendus Geosci 345(11-12):427–438

    Article  Google Scholar 

  • Chauhan S et al (2010) Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model. Landslides 7(4):411–423

    Article  Google Scholar 

  • Chung C-JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard map**. Nat Hazards 30(3):451–472

    Article  Google Scholar 

  • Dahigamuwa T et al (2016) Feasibility study of land cover classification based on normalized difference vegetation index for landslide risk assessment. Geosciences 6(4):45

    Article  Google Scholar 

  • Das S (2020) Flood susceptibility map** of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Remote Sens Appl Soc Environ 20:100379

    Google Scholar 

  • Dou J et al (2015) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS One 10(7):e0133262

    Article  Google Scholar 

  • Emerton R et al (2016) Continental and global scale flood forecasting systems. WIREs Water 3:391–418

    Article  Google Scholar 

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41(6):720–730

    Article  Google Scholar 

  • Fisher PF (1991) First experiments in viewshed uncertainty: the accuracy of the viewshed area. Photogramm Eng Remote Sens 57(10):1321–1327

    Google Scholar 

  • Fisher PF, Tate NJ (2006) Causes and consequences of error in digital elevation models. Prog Phys Geogr 30(4):467–489

    Article  Google Scholar 

  • Fleuchaus P et al (2021) Retrospective evaluation of landslide susceptibility maps and review of validation practice. Environ Earth Sci 80(15):1–15

    Article  Google Scholar 

  • Frattini P et al (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111(1-4):62–72

    Article  Google Scholar 

  • Galli M et al (2008) Comparing landslide inventory maps. Geomorphology 94(3-4):268–289

    Article  Google Scholar 

  • Gan F et al (2018) Water and soil loss from landslide deposits as a function of gravel content in the Wenchuan earthquake area, China, revealed by artificial rainfall simulations. PLoS One 13(5):e0196657

    Article  Google Scholar 

  • Gholami M et al (2019) Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index method. J Earth Syst Sci 128:42–22

    Article  Google Scholar 

  • González-Benito Ó (2002) Overcoming data limitations for store choice modelling.: exploiting retail chain choice data by means of aggregate logit models. J Retail Consum Serv 9(5):259–268

    Article  Google Scholar 

  • Henriques C et al (2015) The role of the lithological setting on the landslide pattern and distribution. Eng Geol 189:17–31

    Article  Google Scholar 

  • Herve, V. H., et al. (2015). "Integrated flood modeling for flood hazard assessment in Kigali City, Rwanda." GeoTechRwanda

    Google Scholar 

  • Hong H et al (2018) Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci Total Environ 625:575–588

    Article  Google Scholar 

  • Huggins C (2009) Agricultural policies and local grievances in rural Rwanda. Peace Rev 21(3):296–303

    Article  Google Scholar 

  • Intrawichian N, Dasananda S (2011) Frequency ratio model based landslide susceptibility map** in lower Mae Chaem watershed northern Thailand. Environ earth sci 64 (8): 2271–2285. J Geol Soc India 81(2):219231

    Google Scholar 

  • Jaafari A et al (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

    Article  Google Scholar 

  • Jebur MN et al (2014) Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale. Remote Sens Environ 152:150–165

    Article  Google Scholar 

  • Khosravi K et al (2016) Flash flood susceptibility analysis and its map** using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ Monit Assess 188(12):1–21

    Article  Google Scholar 

  • Kumar R, Anbalagan R (2016) Landslide susceptibility map** using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. J Geol Soc India 87(3):271–286

    Article  Google Scholar 

  • Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility map** using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491

    Article  Google Scholar 

  • Lee S, Sambath T (2006) Landslide susceptibility map** in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855

    Article  Google Scholar 

  • Li C et al (2021) How will Rwandan land use/land cover change under high population pressure and changing climate? Appl Sci 11(12):5376

    Article  Google Scholar 

  • Luzi L, Pergalani F (1999) Slope instability in static and dynamic conditions for urban planning: the ‘Oltre Po Pavese’case history (Regione Lombardia–Italy). Nat Hazards 20(1):57–82

    Article  Google Scholar 

  • Maniraho AP et al (2021) Application of the adapted approach for crop management factor to assess soil erosion risk in an agricultural area of Rwanda. Land 10(10):1056

    Article  Google Scholar 

  • Manyifika M (2015) Diagnostic assessment on urban floods using satellite data and hydrologic models in Kigali. University of Twente, Rwanda

    Google Scholar 

  • Meten M et al (2015) GIS-based frequency ratio and logistic regression modelling for landslide susceptibility map** of Debre Sina area in Central Ethiopia. J Mt Sci 12(6):1355–1372

    Article  Google Scholar 

  • MIDIMAR (2015) The National Risk Atlas of Rwanda. Nairobi, Ministry of Disaster Management and Refugee Affairs

    Google Scholar 

  • Mohammady, M., et al. (2012). "Landslide susceptibility map** at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models." J Asian Earth Sci 61: 221-236

    Google Scholar 

  • Muhire I et al (2015) Spatio-temporal variations of rainfall erosivity in Rwanda. Journal of Soil Science and Environmental Management 6(4):72–83

    Google Scholar 

  • Munyaneza O et al (2013) Hydraulic structures Design for Flood Control in the Nyabugogo wetland, Rwanda. Kigali, Rwanda

    Google Scholar 

  • Muyombano E (2019) Livelihood and food security of vulnerable people with limited or no land in northern Rwanda: a land use consolidation programme analysis. Ghana Journal of Geography 11(2):103–126

    Google Scholar 

  • Nahayo L et al (2019) Landslides hazard map** in Rwanda using bivariate statistical index method. Environ Eng Sci 36(8):892–902

    Article  Google Scholar 

  • Nakileza BR, Nedala S (2020) Topographic influence on landslides characteristics and implication for risk management in upper Manafwa catchment, Mt Elgon Uganda. Geoenvironmental Disasters 7(1):1–13

    Article  Google Scholar 

  • Nsengiyumva JB et al (2019a) Comparative analysis of deterministic and semiquantitative approaches for shallow landslide risk modeling in Rwanda. Risk Anal 39(11):2576–2595

    Article  Google Scholar 

  • Nsengiyumva JB et al (2019b) Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-eastern Africa. Sci Total Environ 659:1457–1472

    Article  Google Scholar 

  • Obarein OA, Amanambu AC (2019) Rainfall timing: variation, characteristics, coherence, and interrelationships in Nigeria. Theor Appl Climatol 137(3):2607–2621

    Article  Google Scholar 

  • Papaioannou G et al (2015) Multi-criteria analysis framework for potential flood prone areas map**. Water Resour Manag 29(2):399–418

    Article  Google Scholar 

  • Petrea D et al (2014) The determination of the landslide occurrence probability by GIS spatial analysis of the land morphometric characteristics (case study: the Transylvanian plateau). Carpathian Journal of Earth and Environmental Sciences 9(2):91–102

    Google Scholar 

  • Pham BT et al (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250

    Article  Google Scholar 

  • Piller, A. N. (2016). "Precipitation intensity required for landslide initiation in Rwanda."

    Book  Google Scholar 

  • Pourghasemi HR et al (2018) Analysis and evaluation of landslide susceptibility: a review on articles published during 2005–2016 (periods of 2005–2012 and 2013–2016). Arab J Geosci 11(9):1–12

    Article  Google Scholar 

  • Pradhan AMS, Kim Y-T (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-Ri Creek, South Korea. Nat Hazards 72(2):1189–1217

    Article  Google Scholar 

  • Pradhan B (2010) Flood susceptible map** and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2)

    Google Scholar 

  • Pritchard MF (2013) Land, power and peace: tenure formalization, agricultural reform, and livelihood insecurity in rural Rwanda. Land Use Policy 30(1):186–196

    Article  Google Scholar 

  • Raja NB et al (2017) Landslide susceptibility map** of the Sera River basin using logistic regression model. Nat Hazards 85(3):1323–1346

    Article  Google Scholar 

  • Razavizadeh S et al (2017) Map** landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76(14):1–16

    Article  Google Scholar 

  • Regmi AD et al (2014) Landslide susceptibility map** along Bhalubang—Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5):1266–1285

    Article  Google Scholar 

  • Reichenbach P et al (2014) The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environ Manag 54(6):1372–1384

    Article  Google Scholar 

  • REMA (2013) The assessment of economic impacts of the 2012 wet season flooding in Rwanda, Kigali

    Google Scholar 

  • REMA (2015) State of the environment and outlook report in Rwanda. Greening agriculture with resource efficient, low carbon and climate resilient practices. Government of Rwanda, Kigali

    Google Scholar 

  • Roback K et al (2018) The size, distribution, and mobility of landslides caused by the 2015 Mw7. 8 Gorkha earthquake, Nepal. Geomorphology 301:121–138

    Article  Google Scholar 

  • Romer C, Ferentinou M (2016) Shallow landslide susceptibility assessment in a semiarid environment—a quaternary catchment of KwaZulu-Natal, South Africa. Eng Geol 201:29–44

    Article  Google Scholar 

  • Rupert M et al (2008) Using logistic regression to predict the probability of debris flows in areas burned by wildfires, southern California, 2003-2006. US Geological Survey Washington, DC

    Book  Google Scholar 

  • Saaty T (1980) The analytic hierarchy process: planning, priority setting resource allocation. N Y. McGraw-Hill."

    Google Scholar 

  • Saaty TL, Vargas L (2001) LG 2001. Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, Kluwer, USA

    Google Scholar 

  • Sajadi P et al (2022) Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan plateau region by five learning algorithms. Geoscience Letters 9(1):1–25

    Article  Google Scholar 

  • Samia J et al (2017) Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory. Landslides 14(2):547–558

    Article  Google Scholar 

  • Shafapour Tehrany M et al (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomat Nat Haz Risk 8(2):1538–1561

    Article  Google Scholar 

  • Shafizadeh-Moghadam H et al (2018) Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility map**. J Environ Manag 217:1–11

    Article  Google Scholar 

  • Shirzadi A et al (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76(2):1–18

    Article  Google Scholar 

  • Shirzadi A et al (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility map**. Sensors 18(11):3777

    Article  Google Scholar 

  • Silalahi FES et al (2019) Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters 6(1):1–17

    Article  Google Scholar 

  • Sun X et al (2018) Landslide susceptibility map** using logistic regression analysis along the **sha river and its tributaries close to Derong and Deqin County, southwestern China. ISPRS Int J Geo Inf 7(11):438

    Article  Google Scholar 

  • Tehrany MS et al (2013) Spatial prediction of flood susceptible areas using rule-based decision tree (DT) and a novel ensemble bivariate and multivariate statistical model in GIS. J Hydrol 504:69–79

    Article  Google Scholar 

  • Tehrany MS et al (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125:91–101

    Article  Google Scholar 

  • Uwera M et al (2020) Contribution of green infrastructures on flood risk reduction in Kigali City of Rwanda. International Journal of Environmental Planning and Management 6(4):115–124

    Google Scholar 

  • Van Westen CJ et al (1997) Prediction of the occurrence of slope instability phenomenal through GIS-based hazard zonation. Geol Rundsch 86(2):404–414

    Article  Google Scholar 

  • Walker LR, Shiels AB (2013) Physical causes and consequences for landslide ecology

    Google Scholar 

  • Wallerstein N, Arthur S (2012) Improved methods for predicting trash delivery to culverts protected by trash screens. Journal of Flood Risk Management 5(1):23–36

    Article  Google Scholar 

  • Weatherspoon DD et al (2021) Rwanda’s commercialization of smallholder agriculture: implications for rural food production and household food choices. Journal of Agricultural & Food Industrial Organization 19(1):51–62

    Article  Google Scholar 

  • Wechsler S (2007) Uncertainties associated with digital elevation models for hydrologic applications: a review. Hydrol Earth Syst Sci 11(4):1481–1500

    Article  Google Scholar 

  • Wilson JP (2012) Digital terrain modeling. Geomorphology 137(1):107–121

    Article  Google Scholar 

  • Yalcin A, Bulut F (2007) Landslide susceptibility map** using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41(1):201–226

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility map**: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3-4):251–266

    Article  Google Scholar 

  • Zaibon S et al (2017) Soil water infiltration affected by topsoil thickness in row crop and switchgrass production systems. Geoderma 286:46–53

    Article  Google Scholar 

  • Zêzere J et al (2017) Map** landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267

    Article  Google Scholar 

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Li, L., Mind’je, R. (2023). Susceptibility Modeling and Map**. In: Hydrogeological Hazard Susceptibility and Community Risk Perception in Rwanda. Springer, Singapore. https://doi.org/10.1007/978-981-99-1751-8_5

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