Ayele Almaw Fenta

Bio: Ayele Almaw Fenta is an academic researcher from Tottori University. The author has contributed to research in topics: Surface runoff & Environmental science. The author has an hindex of 17, co-authored 36 publications receiving 1030 citations. Previous affiliations of Ayele Almaw Fenta include Mekelle University.

Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia

Abstract: Sustainable development and management of groundwater resources require application of scientific principles and modern techniques. An integrated approach is implemented using remote sensing and geographic information system (GIS)-based multi-criteria evaluation to identify promising areas for groundwater exploration in Raya Valley, northern Ethiopia. The thematic layers considered are lithology, lineament density, geomorphology, slope, drainage density, rainfall and land use/cover. The corresponding normalized rates for the classes in a layer and weights for thematic layers are computed using Saaty’s analytical hierarchy process. Based on the computed rates and weights, aggregating the thematic maps is done using a weighted linear combination method to obtain a groundwater potential (GP) map. The GP map is verified by overlay analysis with observed borehole yield data. Map-removal and single-parameter sensitivity analyses are used to examine the effects of removing any of the thematic layers on the GP map and to compute effective weights, respectively. About 770 km2 (28 % of the study area) is designated as ‘very good’ GP. ‘Good’, ‘moderate’ and ‘poor’ GP areas cover 630 km2 (23 %), 600 km2 (22 %) and 690 km2 (25 %), respectively; the area with ‘very poor’ GP covers 55 km2 (2 %). Verification of the GP map against observed borehole yield data shows 74 % agreement, which is fairly satisfactory. The sensitivity analyses reveal the GP map is most sensitive to lithology with a mean variation index of 6.5 %, and lithology is the most effective thematic layer in GP mapping with mean effective weight of 52 %.

The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia

Abstract: Information on the rate and pattern of urban expansion is required by urban planners to devise proper urban planning and management policy directions. This study evaluated the dynamics and spatial pattern of Mekelle City’s expansion in the past three decades 1984–2014. Multi-temporal Landsat images and Maximum Likelihood Classifier were used to produce decadal land use/land cover LULC maps. Changes in LULC and spatial pattern of urban expansion were analysed by post-classification change detection and spatial metrics, respectively. The results showed that in the periods 1984–1994, 1994–2004, and 2004–2014, the built-up area increased annually by 10%, 9%, and 8%, respectively; with an average annual increment of 19% 100 ha year−1, from 531 ha in 1984 to 3524 ha in 2014. Between 1984 and 2014, about 88% of the gain in built-up area was from conversion of agricultural lands, which decreased by 39%. Extension of existing urban areas was the dominant growth type, which accounted for 54%, 75%, and 81% of the total new development during 1984–1994, 1994–2004, and 2004–2014, respectively. The spatial metrics analyses revealed urban sprawl, with increased heterogeneity and gradual dispersion in the outskirts of the city. The per capita land consumption rate ha per person increased from 0.009 in 1984 to 0.014 in 2014, indicating low density urban growth. Based on the prediction result, the current 2014 built-up area will double by 2035, and this is likely to have multiple socioeconomic and environmental consequences unless sustainable urban planning and development policies are devised.

Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing

Abstract: As demands for groundwater in the arid and semi-arid areas increase, delineation of groundwater potential zone becomes an increasingly valuable technique for implementing a successful groundwater potential analysis. The capability of using weights-of-evidence (WOE) and evidential belief function (EBF) models for groundwater potential mapping is tested and compared in the Ilam Plain, Iran. In the present study, multiple geo-environmental factors including lithology, land use, distance from river, soil texture, drainage density, altitude, curvature, topographic wetness index (TWI), slope percent, lineament density, and rainfall were used as inputs for both models. Subsequently, a well inventory map was produced using documentary sources of Iranian Water Resources Department (IWRD) and extensive field surveys. About 145 groundwater productivity data (with high potential yield values of ≥11 m3/h) were separated from well locations. Out of these, 101 (70 %) cases were randomly selected for groundwater potential modeling, and the remaining 44 (30 %) cases were applied for the validation purpose. In the next step, groundwater potential maps were produced using WOE and EBF models in GIS environment. The receiver operating characteristic (ROC) curves for the produced maps were drawn and the areas under the curves (AUC) were determined. From the analysis, predictive performance of EBF model (AUC = 83.7 %) was better than of WOE model (AUC = 78.2 %). The results also show the capability of EBF model in managing uncertainty associated in groundwater potential mapping. Therefore, WOE and EBF models are shown to be an effective prediction models for groundwater potential mapping. The groundwater potential map can be helpful for planners in groundwater management and land use planning.