Publications by authors named "Asnake Mekuriaw"
Article Synopsis
- The study focuses on mapping the spatial distribution of a species in the highlands of northern Ethiopia, highlighting its historical uses and the controversies over its environmental impact.
- The researchers utilized Sentinel-2 data along with various machine learning algorithms (Random Forest, Support Vector Machine, Boosted Regression Trees) to create and validate a model for this purpose, using a significant data set of 419 georeferenced points.
- Random Forest emerged as the most effective algorithm, achieving high accuracy in predictions, primarily influenced by factors like the Green Normalized Difference Vegetation Index, elevation, and proximity to roads.
The aim of this study was to evaluate the performance of CHIRPS and TAMSAT satellite rainfall data over the Upper Gelana watershed, where gauged meteorological data to understand the nature of the climate are scarce. In addition, variability and trends in rainfall and temperature were examined from 1983 to 2021. To evaluate satellite rainfall, categorical and continuous validation statistics were used.
View Article and Find Full Text PDFWoody vegetation plays a vital role in regulating the water budget and energy exchange in the Earth's system. This study aimed at analyzing the spatiotemporal variability of Normalized Difference Vegetation Index (NDVI) and its response to Potential Evapotranspiration (PET), rainfall (RF), soil moisture (SM), and temperature (TEM) in the study area. The trends, correlations, and relationships between NDVI and climate variables were executed using Mann-Kendall monotonic trend (MKMT), partial correlation coefficients (PCC), and multiple linear regression (MLR) methods, respectively.
View Article and Find Full Text PDFProperty crime has become a challenge in major cities of developing countries including Addis Ababa, Ethiopia. However, factors contributing to property crime have not been carefully examined. Therefore, this paper presents the physical and socio-economic factors that clearly have a substantial impact on property crime.
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