Addis Kifle

Bio: Addis Kifle is an academic researcher from Mekelle University. The author has contributed to research in topics: Thematic map. The author has an hindex of 1, co-authored 1 publications receiving 90 citations.

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 %.

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.

Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China

Abstract: Bailongjiang watershed in southern Gansu province, China, is one of the most landslide-prone regions in China, characterized by very high frequency of landslide occurrence. In order to predict the landslide occurrence, a comprehensive map of landslide susceptibility is required which may be significantly helpful in reducing loss of property and human life. In this study, an integrated model of information value method and logistic regression is proposed by using their merits at maximum and overcoming their weaknesses, which may enhance precision and accuracy of landslide susceptibility assessment. A detailed and reliable landslide inventory with 1587 landslides was prepared and randomly divided into two groups, (i) training dataset and (ii) testing dataset. Eight distinct landslide conditioning factors including lithology, slope gradient, aspect, elevation, distance to drainages, distance to faults, distance to roads and vegetation coverage were selected for landslide susceptibility mapping. The produced landslide susceptibility maps were validated by the success rate and prediction rate curves. The validation results show that the success rate and the prediction rate of the integrated model are 81.7 % and 84.6 %, respectively, which indicate that the proposed integrated method is reliable to produce an accurate landslide susceptibility map and the results may be used for landslides management and mitigation.

Delineation of groundwater potential zones in a drought-prone semi-arid region of east India using GIS and analytical hierarchical process techniques

Abstract: Over-extraction of groundwater has compromised its climatic resilience properties and the arid/semi-arid rural tracts are becoming increasingly vulnerable to the risks of groundwater scarcity. This study has employed a combination of Geographical Information System (GIS) and Analytical Hierarchical Process (AHP) techniques to delineate the Groundwater Potential Zones (GPZs) of the semi-arid Birbhum district in eastern India which suffers from seasonal drought during lean periods. For a reliable evaluation, a large number of thematic layers (N = 12) including geology, geomorphology, Land Use/Land Cover (LULC), fault and lineament density, drainage density, rainfall, soil type, slope, roughness, topographic wetness index, topographic position index and curvature were considered for this assessment. Multicollinearity and consistency checks were performed prior integrating the layers to avoid a non-trivial degree of accuracy in prediction output. The GPZ map was obtained with an accuracy of 80.49% with respect to the observation tube well data. Based on the obtained output, 38.24%, 24.24% and 11.14% of areas of the district classified as moderate, poor, and very poor GPZs, respectively, whereas only 26.38% of the district classified as high to very high GPZs. Cross-validation using the Receiver Operating Characteristic curve revealed a good prediction accuracy of 71.50%. Furthermore, map removal and single parameter sensitivity analysis was also performed which revealed geology, geomorphology, soil types, rainfall, LULC and lineament density as the most influential parameters for the prediction model where exclusion of any thematic layer significantly changes the prediction accuracy and area of each GPZ class. The most convincing GPZs are recorded in some parts of the Mayurakshi and Ajay river basins and certain alluvial aquifer regions. Nonetheless, the study recommends the adaptation of Managed Aquifer Recharge techniques including rainwater harvesting, alternative cropping patterns and irrigation techniques such as sprinklers, drips and micro irrigations to increase the groundwater potential of the water crisis zones.