International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 03 | Mar 2024
p-ISSN: 2395-0072
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GIS and Fuzzy Logic Modeling for Assessing Landslide Susceptibility in Kenya Hafid Nanis1, Mohamed Aly2 1Department of Geosciences, University of Arkansas, Fayetteville, Arkansas USA & College of Engineering
Technology, Zuwara Libya
2Associate Professor, Department of Geosciences, University of Arkansas, Fayetteville, Arkansas USA
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Abstract - Landslide susceptibility modeling plays a crucial
The occurrence of landslides is not limited to mountainous regions; they can happen in areas with low relief as well. Weather and climate patterns play a crucial role in landslide occurrence. Landslides are influenced by a combination of external triggering factors, such as over-steepened slopes, heavy rainfall, earthquakes, or volcanic eruptions, and internal inherent factors, including geological and morphological conditions. Geological factors comprise weak and weathered bedrock, jointed materials, fault lines, and variations in permeability. Human activities, such as improper land use, deforestation, excavation, and changes in groundwater systems, also contribute to landslides [2] [3] (Marshak, 2019, Varnes, 1978).
role in sustainable development and hazard mitigation strategies. Despite the presence of numerous landslide-prone areas in Kenya, previous research has failed to address them adequately. This study aims to fill this research gap by developing a comprehensive methodology for assessing landslide susceptibility in Kenya, employing a weighted Geographic Information System (GIS) and a fuzzy logic model. The major causative factors influencing landslides, including lithology, slope, elevation, soil type, land-cover, precipitation, distance to fault lines, distance to major drainages, distance to roads, and distance to earthquake-occurrence locations, were carefully investigated and weighted using the Analytical Hierarchy Process (AHP). The findings reveal that approximately 26% of Kenya's total area is susceptible to landslides. Through the integration of the weighted overlay and fuzzy logic models, four distinct landslide-vulnerability zones were identified: low, medium, high, and very high. To validate the models, a dataset of 130 historical landslides was employed. Remarkably, the highest zone of landslide vulnerability identified by the weighted overlay and fuzzy logic models coincided with about 97% and 85% of the past landslide events, respectively. These results attest to the reliability of the developed models and their potential to contribute to future planning and the mitigation of landslide hazards in Kenya.
Kenya has experienced significant landslide incidents, primarily in the central highlands and southwestern regions (Figure 1). Heavy rainfall-induced landslides are common in the country [4] (Davies and Nyambok, 1993). The relationship between landslides and precipitation rates is evident, with notable events occurring during years with heavy rains. Anthropogenic factors, driven by population growth, have led to land degradation and an increase in landslide-prone areas [5] [6] (Larsson, 1989; Rob, 2011). Deforestation, particularly in districts bordering mountainous regions, exacerbates the problem [7] (MainaGichaba et al., 2013). While seismic and volcanic activities have not recently triggered landslides in Kenya, historical records indicate past occurrences associated with a 6.9 M earthquake in Subukia in 1928 [7] [8] (Maina-Gichaba et al., 2013; Mulwa et al., 2013).
Key Words: Landslide susceptibility, GIS, Kenya, weighted overlay, fuzzy logic
The rise in landslide activities since the 1980s has resulted in severe social, economic, and environmental consequences, including loss of life, damage to agriculture, infrastructure, and property [9] [10] (Davies, 1996; Rowntree, 1989). Although available records indicate approximately 233 fatalities from landslides between 1986 and 2018, the actual number is likely much higher. Unfortunately, there is a lack of comprehensive data on the economic losses caused by landslides in Kenya, despite significant destruction to buildings, roads, railways, and waterways [11] (Ngecu and Mathu, 1999). The urgent need for reconstruction and assistance following landslides in western Kenya was estimated at $18 million in 2013. Landslides also have detrimental environmental effects, with debris flows polluting rivers and impacting domestic livestock.
1.INTRODUCTION Landslides pose significant threats worldwide, resulting in substantial economic losses, environmental damage, and societal impacts. With the expanding population and the encroachment of settlements into hazardous areas, the risks associated with landslides have intensified [1] [2] (Mugagga et al., 2012; Marshak, 2019). Geologists define landslides as the downslope movement of rocks or regolith (loose unconsolidated rock and soil) due to the force of gravity. These mass movements exhibit various characteristics, including composition, rate of movement, coherence, and the environment in which they occur, such as subaerial or submarine settings.
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