Landslide susceptibility and influencing factors analysis in Rwanda

Mind’je, Richard; Li, Lanhai; Nsengiyumva, Jean Baptiste; Mupenzi, Christophe; Nyesheja, Enan Muhire; Kayumba, Patient Mindje; Gasirabo, Aboubakar; Hakorimana, Egide

doi:10.1007/s10668-019-00557-4

Cited by 61 publications

(29 citation statements)

References 88 publications

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“…The inventory map consisted of old landslides and landslides that just happened in Cimanggung, Sumedang. The landslide inventory is a fundamental step to assess landslide susceptibility, which contains information about the location and timing of landslides [14], [39].…”

Section: B Data and Research Methodologymentioning

confidence: 99%

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Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

Sukristiyanti¹,

Wikantika²,

Sadisun³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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A landslide susceptibility mapping is essential for landslide hazard mitigation to reduce the associated risk. This paper aims to present the results of the landslide susceptibility modeling in the Citarik sub-watershed using three bivariate statistical-based methods, i.e., frequency ratio (FR), information value (IV), and weight of evidence (WoE). The main objective of this study is to evaluate the significance of the threshold of the area under curve (AUC) value in parameter selection. In this study, 118 landslide pixels were compiled from Google Earth images, unmanned aircraft vehicle (UAV) aerial photos taken just after the landslide, official landslide reports, and field observation. Thirteen landslide causative factors were prepared in Geographic Information System (GIS) environment, derived from various satellite images and maps. The landslide data were divided into two groups, 70% of data as training data and the rest as test data. Two scenarios that involve a different number of parameters were compared to explain the threshold of the AUC value in parameter selection and model accuracy. The result of this study shows that the AUC value threshold of 0.6 for parameter selection cannot be applied in all cases, and the performance of both two scenarios was excellent in assessing landslide susceptibility in this study area. Those three landslide susceptibility zonation maps of the best scenario showed that the sub-watershed's northern, northeastern, south-eastern, and southern parts were under high to very high susceptibility to landslides, including the Cimanggung area where a recent deadly double landslide occurred.

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Section: B Data and Research Methodologymentioning

confidence: 99%

“…Frequency ratio [7], [12]- [14], information value [12], [15], [16], and the weight of evidence [17]- [20] are some bivariate statistics-based methods. Meanwhile, logistic regression [13], [21]- [23] and probit regression [17] are multivariate statistics-based methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

Sukristiyanti¹,

Wikantika²,

Sadisun³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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“…The selection of Landsat imagery was influenced by its quality particularly for those with limited or low cloud cover. The acquired image was then used to perform classification using the maximum likelihood classification (MLC) technique in ENVI software version 5.5.2 (Mind'je et al 2019a(Mind'je et al , 2019b. The main emphasis for accuracy assessment pixel samples was on areas that could be visibly identified on Google Earth imagery.…”

Section: Datasetsmentioning

confidence: 99%

“…Features such as steep slopes and uncontrolled terrain speed up surface runoff and so much of the rainwater go unevenly. Consequently, the country is often perceived to be susceptible to increased erosion, flooding, and drought especially in the Nyabugogo catchment area (Mind'je et al 2019a(Mind'je et al , 2019b. Owing to the above, the RWH system is the best technique, which can be effectively used to trap unused surface runoff and thereby increase groundwater recharge.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Integrating a GIS-based approach and a SWAT model to identify potential suitable sites for rainwater harvesting in Rwanda

Umugwaneza

Chen

et al. 2022

Journal of Water Supply: Research and Technology-Aqua

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The increasing demand of water results in the overexploitation of water resources. This situation calls for more effective water management alternatives including rainwater harvesting (RWH) systems. Due to the lack of biophysical data and infrastructure, the identification of suitable sites for various RWH systems is a challenging issue. However, integrating geospatial analysis and modeling approaches has become a promising tool to identify suitable sites for RWH. Thus, this study aimed at identifying suitable sites for RWH in the Nyabugogo catchment located in Rwanda by integrating a geo-information-based multi-criteria decision-making (MCDM) and SWAT (Soil and Water Assessment Tool) model. Moreover, the sediment yield was compared to the soil erosion evaluated using the Revised Universal Soil Loss Equation (RUSLE) owing to the lack of sediment concentration measured data. The results revealed that about 4.8 and 16.35% of the study area are classified as highly suitable and suitable areas for RWH, respectively. Around 6% of the study area (98.5 km2) was found to be suitable for farm ponds, whereas 1.6% (26.1 km2) suitable for check dams, and 25.9% (423 km2) suitable for bench terraces. Among 50 proposed sites for the RWH structures, 29 are located in the most suitable area for RWH. The results implicated that the surface runoff, sediment yield, and topography are essential factors in identifying the suitability of RWH areas. It is concluded that the integrated geospatial and MCDM techniques provide a useful and efficient method for planning RWH at a basin scale in the study area.

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“…It is a well-known fact that each study area has its specific geomorphological features. By analyzing the correlation of the causal factors, factors with high overall correlation were excluded (Liu et al, 2019;Mind'je et al, 2020;Zhao and Chen, 2020). However, the remaining causal factors may be highly correlated in some micro-topography parts, which cannot be detected by the overall correlation analysis and have not been mentioned in the literature.…”

Section: Introductionmentioning

confidence: 99%

Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping

Xiao

Tian

et al. 2021

Front. Earth Sci.

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A landslide susceptibility map (LSM) is the basis of hazard and risk assessment, guiding land planning and utilization, early warning of disaster, etc. Researchers are often overly keen on hybridizing state-of-the-art models or exploring new mathematical susceptibility models to improve the accuracy of the susceptibility map in terms of a receiver operator characteristic curve. Correlation analysis of the causal factors is a necessary routine process before susceptibility modeling to ensure that the overall correlation among all factors is low. However, this overall correlation analysis is insufficient to detect a high local correlation among the causal factor classes. The objective of this study is to answer three questions: 1) Is there a high correlation between causal factors in some parts locally? 2) Does it affect the accuracy of landslide susceptibility assessment? and 3) How can this influence be eliminated? To this aim, Wanzhou County was taken as the test site, where landslide susceptibility assessment based on 12 causal factors has been previously performed using the frequency ratio (FR) model and random forest (RF) model. In this work, we conducted a local spatial correlation analysis of the “altitude” and “rivers” factors and found a sizeable spatial overlap between altitude-class-1 and rivers-class-1. The “altitude” and “rivers” factors were reclassified, and then the FR model and RF model were used to reevaluate the susceptibility and analyze the accuracy loss caused by the local spatial correlation of the two factors. The results demonstrated that the accuracy of LSMs was markedly enhanced after reclassification of “altitude” and “rivers,” especially for the RF model–based LSM. This research shed new light on the local correlation of causal factors arising from a particular geomorphology and their impact on susceptibility.

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Landslide susceptibility and influencing factors analysis in Rwanda

Cited by 61 publications

References 88 publications

Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

Evaluation of Parameter Selection in the Bivariate Statistical-based Landslide Susceptibility Modeling (Case Study: the Citarik Sub-watershed, Indonesia)

Integrating a GIS-based approach and a SWAT model to identify potential suitable sites for rainwater harvesting in Rwanda

Reducing Local Correlations Among Causal Factor Classifications as a Strategy to Improve Landslide Susceptibility Mapping

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