A GIS-based study has been carried out to map areas landslide susceptibility using both frequency ratio (FR) and Shannon entropy (SE) bivariate statistical models. A total of 270 landslides were identified and classified randomly into training landslides datasets (70%) and the remaining (30%) of landslides datasets were used for validation purpose. The 11 landslides conditioning factors like slope, elevation, aspect, curvature, topographic wetness index, normalized difference vegetation index, distance from road, distance from river, distance from faults, land use, and rainfall were integrated with training landslides to determine the weights of each landslide conditioning factor and factor classes using both frequency ratio and Shannon entropy models. The landslide susceptibility maps were produced by overlay the weights of all the landslide conditioning factors using raster calculator of the spatial analyst tool in ArcGIS 10.4. The final landslide susceptibility maps were reclassified as very low, low, moderate, high, and very high susceptibility classes both FR and SE models. This susceptibility maps were validated using landslide area under the curve (AUC). The results of AUC accuracy models showed that the success rates of the FR and SE models were 0.761 and 0.822, while the prediction rates were 0.753 and 0.826, respectively.
In this study an effort has been made to identify suitable sites for safe disposal. For proper identification and selection of appropriate sites for solid waste disposal careful and systematic procedures need to be adopted and followed. The main objective of this research was identified the suitable solid waste disposal site by using the GIS-based approaches in Debre markos town. The present study had considered various factors like road networks; rivers, soil, slope, altitude and land use/ land cover for selecting a suitable solid waste disposal site within the study area. The relative weights of the factor were estimated using AHP and factor maps were developed by using GIS spatial operations. The final weighted factor map produced an overall solid waste disposal suitability map. The solid waste disposal site suitability map was presented in four suitability index such as highly suitable, moderately suitable, low suitable and unsuitable. The result shows that around 21% area is highly suitable for solid waste disposal site, 25% is moderately suitable, 28% of study area has low suitable and 26% area is unsuitable for solid waste disposal site.
The siting of wastewater treatment plant is an extremely complex task mainly due to the fact that the identification and selection process involves many factors and strict regulations. For proper identification and selection of appropriate sites for wastewater treatment plants careful and systematic procedures need to be adopted and followed. The main objective of this research was to find the environmentally and economically suitable location of wastewater treatment plant by using the GIS-based MCA approaches in Bahir Dar town. The present study had integrated road networks, rivers/streams, lake, geology, soil, slope, elevation, wind direction, groundwater wells, groundwater table, rural settlements, urban settlements and land use/ land cover for selecting a suitable wastewater treatment plant site within the study area. The relative weights of the factor were estimated using AHP and criteria maps were developed by using GIS spatial operations. Weighted linear combination was used to integrate weight with the factor maps. The final integration of weighted factor map and constraint map produced an overall wastewater treatment plant suitability map. The wastewater treatment plant site suitability map was presented in four suitability index such as highly suitable, moderately suitable, low suitable and unsuitable. The result shows that around 6.08% area is highly suitable for wastewater treatment plant site, 1.83% is moderately suitable, 0.61% of study area has low suitability and 91.48% area is unsuitable for wastewater treatment plant site. Among the highly suitable areas, five sites were selected and revaluated in terms of road network, river/streams, lake, geology, soil, slope, elevation, wind direction, groundwater wells, groundwater table, rural settlements, urban settlements and land use/land cover to select the optimal suitable site. Finally, site 4 was proposed as the most preferred option for the construction of the wastewater treatment plant site with the minimum effects on economic, environment risk and public health.
Malaria is one of the most severe public health problems worldwide with 300 to 500 million cases and about one million deaths reported to date, 90% of which were reported from Sub Saharan African countries like Ethiopia. The main objective of the study was Assessment of malaria risk areas by using the GIS-based MCA approaches in East Gojjam zone. Weighted overlay technique of multi-criteria analysis was used to develop the malaria risk map. The malaria risk map was produced depending upon the overlay analysis of the malaria hazard map and some factors like land use land cover, population density, health stations. The malaria risk map was classified into four suitability index such as very highly suitable, high suitable, moderately suitable, and low suitable. The result shows that around 21.02% areas is very highly suitable for malaria risk, 24.49% is high suitable, 24.66% is moderately suitable and 29.83 % is low suitable for malaria risk areas. It is suggested that effective identification and mapping of malaria risk areas can be made using geospatial tools, to contribute for the prevention system easily manageable and controlling the disease.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.