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Research Article
Assessing Soil Erosion and Sediment Yield in the Aguat Wuha Dam Catchment, Northwest Ethiopia Using RUSLE and GIS
Getie Amsal Yigzaw*
,
Biniyam Taye Alamrew,
Adna Ashebir,
Ephrem Getahun,
Likinaw Mengstie
Issue:
Volume 10, Issue 2, June 2025
Pages:
29-53
Received:
3 March 2025
Accepted:
31 March 2025
Published:
29 April 2025
Abstract: One crucial metric for estimating a reservoirs and dam’s lifespan is sedimentation. It is dependent upon sediment output, which in turn is dependent upon soil erosion. The study area, the Aguat Wuha Dam, was located in Simada woreda, of northwestern parts of Ethiopia. And the study's goal was to use Arc GIS and RUSLE adjusted to Ethiopian conditions to assess potential soil erosion and sediment output from the watershed and identify hotspot locations for appropriate planning for erosion and sedimentation problem management techniques to make the outputs of the dam project more productive and effective for the proposed and suggested purpose of the dam. To predict the geographical patterns of soil erosion in the watershed, the Geographic Information System (GIS) was combined with the revised universal soil loss equation (RUSLE). A soil erosion map was produced using ArcGIS by utilizing all of the model's parameters, including Erosivity, erodibility, steepness, land use, land cover, and supportive practice factors. The watershed's yearly soil loss varies from 0 to 413.86 tons/ha. In order to determine the erosion hotspot area, the average annual soil loss value was discovered to be 9.24 tons/ha/year and was categorized into six erosion severity classes: low, moderate, high, very high, severe, and very severe. These findings indicated that 162.57 ha and 699.17 ha of the watershed were considered to be extremely and severely vulnerable to soil erosion, respectively. It was discovered that the anticipated sediment yield supplied to the outlet varied from 0 to 104.94 tons/ha/year. By standing from the implications of the assessments of the geological, geotechnical, topographical, and socioenvironmental considerations Watershed management is the most effective way to reduce the amount of sediment produced and the amount that enters the reservoir among the several reservoir sedimentation control options that are available.
Abstract: One crucial metric for estimating a reservoirs and dam’s lifespan is sedimentation. It is dependent upon sediment output, which in turn is dependent upon soil erosion. The study area, the Aguat Wuha Dam, was located in Simada woreda, of northwestern parts of Ethiopia. And the study's goal was to use Arc GIS and RUSLE adjusted to Ethiopian condition...
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Research Article
Risk Factor and Spatial Pattern Analysis of Neonatal Mortality in Kenya
Getrude Moraa Nyabuto,
Bonface Malenje,
Anthony Wanjoya
Issue:
Volume 10, Issue 2, June 2025
Pages:
54-65
Received:
18 April 2025
Accepted:
29 April 2025
Published:
3 June 2025
DOI:
10.11648/j.ajmcm.20251002.12
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Views:
Abstract: Neonatal health is a critical component of overall public health, providing the groundwork for a healthy life and making a substantial contribution to the social and economic advancement of any nation. Despite the progress that has been made in reducing the global neonatal mortality rate, substantial regional disparities persist, particularly in Sub-Saharan Africa. In Kenya, the NMR stands at 21 deaths per 1,000 live births (as of 2022) which is higher than the global average. The main objective for this study was to perform risk factor and spatial pattern analysis of neonatal mortality in Kenya. A multivariate logistic regression model was fitted that identified urban residence, underweight birth weight status, unimproved water sources, and non-hospital deliveries (especially in non standard locations) as the significant contributors of neonatal mortality in Kenya. Getis-Ord Gi statistics identified Wajir, Garissa, and Lamu counties as major hotspots in Kenya after showing a strong spatial clustering of high neonatal mortality rates. GWLR, utilized in this study, revealed that climatic factors, such as temperature and aridity, impact neonatal mortality differently across regions in Kenya. Generally, higher temperatures are a significant risk factor for neonatal mortality, particularly in arid counties like Mandera, Wajir, Garissa, Tana River, and Lamu.
Abstract: Neonatal health is a critical component of overall public health, providing the groundwork for a healthy life and making a substantial contribution to the social and economic advancement of any nation. Despite the progress that has been made in reducing the global neonatal mortality rate, substantial regional disparities persist, particularly in Su...
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Research Article
Generative Adversarial Network Based Visual Saliency Prediction with Cascaded Hierarchical Atrous Spatial Pyramid Pooling
Daniel Dufera,
Felmeta Abate*
Issue:
Volume 10, Issue 2, June 2025
Pages:
66-73
Received:
16 January 2025
Accepted:
3 May 2025
Published:
16 June 2025
DOI:
10.11648/j.ajmcm.20251002.13
Downloads:
Views:
Abstract: Visual saliency refers to an area of an image that attracts human attention. The Human Visual System (HVS) can focus on specific parts of a scene, rather than the whole image. Visual attention describes a set of cognitive procedures that choose important information and filter out unnecessary information from cluttered visual scenes. Images become a soul in computer vision since it contains plenty of information and human beings receive 80% of information through vision. In processing the whole image while only a certain part of an image is needed, more resources are consumed. Instead of processing the whole pixels of an image, specifying only the needed pixel is computationally efficient to minimize the efforts. This is achieved by using GAN with CHASPP module and EfficientNet-B7 which uniformly scales up all dimensions of the image (depth, width, and resolution) is selected as feature extractor in this study which improves the way of extracting features in visual saliency prediction. Different datasets like CAT2000, MIT1003, DUTOMRON, and PASCALS are used in this study to illustrate the efficiency of the selected models and techniques. In this study, we developed effective visual saliency prediction using GAN with CHASPP and other factors like edge loss and perceptual loss. CHASPP module scored the best result on the same datasets measured by different evaluation metrics.
Abstract: Visual saliency refers to an area of an image that attracts human attention. The Human Visual System (HVS) can focus on specific parts of a scene, rather than the whole image. Visual attention describes a set of cognitive procedures that choose important information and filter out unnecessary information from cluttered visual scenes. Images become ...
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