Predicting Landslide Susceptibility in Cross River State of Nigeria using Machine Learning

Domaine:

geospatial

Type de record:

paper

Créateur:

JoeDevJosSyl

Éditeur:

Cop

Hôte:

Landslides have continued to wreck its havoc in many parts of the globe; comprehensive studies of landslide susceptibilities of many of these areas are either lacking or inadequate. Hence, this study was aimed at predicting landslide susceptibility in Cross River State of Nigeria, using machine learning. Precisely, the frequency ratio (FR) model was adopted in this study. In adopting this approach, a landslide inventory map was developed using 72 landslide locations identified during fieldwork combined with other relevant data sources. Using appropriate geostatistical analyst tools within a geographical information environment, the landslide locations were randomly divided into two parts in the ratio of 7:3 for the training and validation processes respectively. A total of 12 landslide causing factors, such as; elevation, slope, aspect, profile curvature, plan curvature, topographic position index, topographic wetness index, stream power index, land use/land cover, geology, distance to waterbody and distance to major roads, were selected and used in the spatial relationship analysis of the factors influencing landslide occurrences in the study area. FR model was then developed using the training sample of the landslide to investigate landslide susceptibility in Cross River State which was subsequently validated. It was found out that the distribution of landslides in Cross River State of Nigeria was largely controlled by a combined effect of geo-environmental factors such as elevation of 250 – 500m, slope gradient of >35o, slopes facing the southwest direction, decreasing degree of both positive and negative curvatures, increasing values of topographic position index, fragile sands, sparse vegetation, especially in settlement and bare surfaces areas, distance to waterbody and major road of < 500m. About 46% of the mapped area was found to be at landslide susceptibility risk zones, ranging from moderate – very high levels. The susceptibility model was validated with 90.90% accuracy. This study has shown a comprehensive investigation of landslide susceptibility in Cross River State which will be useful in land use planning and mitigation measures against landslide induced vulnerability in the study area including extrapolation of the findings to proffer solutions to other areas with similar environmental conditions. This is a novel use of a machine learning technique in hazard susceptibility mapping. Keywords: Landslide; Landslide Susceptibility mapping; Cross River State, Nigeria; Frequency ratio, Machine learning

Visit

doi.org

Similaires

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning ModelsPredicting Road Traffic Accident Severity Using Machine Learning Algorithms in Kano State, NigeriaGully Erosion Susceptibility Mapping using Machine Learning Techniques in Ihioma Community, Imo State, NigeriaEvaluation of Machine Learning Models for Predicting Flood Susceptibility Using Spatial and Socio-Environmental Attributes in Lagos, NigeriaGeospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, NigeriaLandslide susceptibility mapping using machine learning techniques and comparison of their performance at Ziz upper watershed, Southeastern Morocco

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models

Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human liv

Predicting Road Traffic Accident Severity Using Machine Learning Algorithms in Kano State, Nigeria

Road traffic accidents (RTAs) are a major global public health and safety crisis, leading to million

Gully Erosion Susceptibility Mapping using Machine Learning Techniques in Ihioma Community, Imo State, Nigeria

Gully erosion poses significant environmental and socio-economic challenges in many regions worldwid

Evaluation of Machine Learning Models for Predicting Flood Susceptibility Using Spatial and Socio-Environmental Attributes in Lagos, Nigeria

International audience Flooding is one of the major challenges to urban resilience in

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing

Landslide susceptibility mapping using machine learning techniques and comparison of their performance at Ziz upper watershed, Southeastern Morocco

Abstract Landslides pose a serious threat to humans, their infrastructure and property, es