Flood vulnerability assessment using GIS at Fetam watershed, upper Abbay basin, Ethiopia

Desalegn, Hunegnaw; Mulu, Arega

doi:10.1016/j.heliyon.2020.e05865

Cited by 67 publications

(37 citation statements)

References 23 publications

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“…The topographic factor is the combination of hill-shade (S) and length of the stream (L). The concept behind the importance of the topographic factor (LS) in estimating the soil loss is that the soil erosion increases with the slope steepness, and the length over which the sensitivity to erosion is valid (Desalegn & Mulu, 2021). The topographic factor (LS) generated from the Digital Elevation Model (DEM) ranges from 0 to 167.…”

Section: Topographic Factor (Ls)mentioning

confidence: 99%

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

Wagari

Tamiru

2021

Air, Soil and Water Research

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In this study, Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) platforms were successfully applied to quantify the annual soil loss for the protection of soil erosion in Fincha catchment, Ethiopia. The key physical factors such as rainfall erosivity ( R-factor), soil erodibility ( K-factor), topographic condition (LS-factor), cover management ( C-factor), and support practice ( P-factor) were prepared in GIS environment from rainfall, soil, Digital Elevation Model (DEM), Land use/Land cover (LULC) respectively. The RUSLE equation was used in raster calculator of ArcGIS spatial tool analyst. The individual map of the derived factors was multiplied in the raster calculator and an average annual soil loss ranges from 0.0 to 76.5 t ha−1 yr−1 was estimated. The estimated annual soil loss was categorized based on the qualitative and quantitative classifications as Very Low (0–15 t ha−1 yr−1), Low (15–45 t ha−1 yr−1), Moderate (45–75 t ha−1 yr−1), and High (>75 t ha−1 yr−1). It was found from the generated soil erosion severity map that about 45% of the catchment area was vulnerable to the erosion with an annual soil loss of (>75 t ha−1 yr−1), and this demonstrates that the erosion reduction actions are immediately required to ensure the sustainable soil resources in the study area. The soil erosion severity map generated based on RUSLE model and GIS platforms have a paramount role to alert all stakeholders in controlling the effects of the erosion. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss protection practices.

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Section: Topographic Factor (Ls)mentioning

confidence: 99%

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

Wagari

Tamiru

2021

Air, Soil and Water Research

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“…As we can see from the reclassified maps, the categories or classifications presented in all maps is based on the studies conducted in different parts of the world and consulted in different pieces of literature [ 4 , 9 , 28 , 29 ]. Random values of weights were assigned to the NN.…”

Section: Resultsmentioning

confidence: 99%

“…In Ethiopia, which is one of the East African countries, more than 1.1 million people have been affected by flooding. A bunch of studies forecasts and predicts that the severity of this natural disaster would be rise significantly in the climate change scenarios [ 9 – 11 ]. Currently, researchers have been emphasizing a long-term contribution to sustainable disaster risk management using geospatial analysis.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Geospatial Analysis for Flood Vulnerability Assessment: A Case of Dire Dawa Watershed, Awash Basin, Ethiopia

Tamiru

Dinka

2021

The Scientific World Journal

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This study presents the novelty artificial intelligence in geospatial analysis for flood vulnerability assessment in Dire Dawa, Ethiopia. Flood-causing factors such as rainfall, slope, LULC, elevation NDVI, TWI, SAVI, K-factor, R-factor, river distance, geomorphology, road distance, SPI, and population density were used to train the ANN model. The weights were generated in the ANN model and prioritized. Initial values were randomly assigned to the NN and trained with the feedforward processes. Ground-truthing points collected from the historical flood events of 2006 were used as targeting data during the training. A rough flood hazard map generated in feedforward was compared with the actual data, and the errors were propagated back into the NN with the backpropagation technique, and this step was repeated until a good agreement was made between the result of the GIS-ANN and the historical flood events. The results were overlapped with ground-truthing points at 88.46% and 89.15% agreement during training and validation periods. Therefore, the application of the GIS-ANN for the assessment of flood vulnerable zones for this city and its catchment was successful. The result of this study can also be further considered along with the city and its catchment for practical flood management.

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“…The density of settlements includes permanent, semi-permanent, and non-permanent houses [22]. At the same time, the number of public and critical facilities contained places of worship, schools, and health service infrastructures.…”

Section: Physical Vulnerabilitymentioning

confidence: 99%

Vulnerability analysis toward river flooding in the Comal Watershed, Indonesia

et al. 2021

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River flooding is a disaster that almost every year hits the downstream Comal watershed. This incident resulted in material, psychological, and even casualties. Therefore, a vulnerability analysis is needed to reduce the impact of flooding. This study aims to analyze the vulnerability to river flooding in the downstream Comal Watershed area, Indonesia. Vulnerability variables used are social, economic, physical, and ecological (environmental) vulnerability following the Indonesian National Disaster Management Agency (BNPB) Regulation 2012. Data were obtained from government agencies and interpretation of SPOT 5 satellite imagery. Data analysis were carried out using scoring and weighting. Moreover, the data classification were using the mean and standard deviation equations. The analysis showed that the vulnerability to river flooding in the Comal watershed area was dominated by a moderate vulnerable class of 10,832.15 hectares (49.69%) and distributed in 26 villages. Furthermore, the high level of vulnerability also covered a large area by 8,773.11 hectares (40.24%), and distributed across 29 villages. Therefore, proper river flooding mitigation planning with the characteristics of the area is needed.

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Flood vulnerability assessment using GIS at Fetam watershed, upper Abbay basin, Ethiopia

Cited by 67 publications

References 23 publications

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection: A Case of Fincha Catchment, Ethiopia

Artificial Intelligence in Geospatial Analysis for Flood Vulnerability Assessment: A Case of Dire Dawa Watershed, Awash Basin, Ethiopia

Vulnerability analysis toward river flooding in the Comal Watershed, Indonesia

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