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Research Article
Gully Erosion Risk Assessment Using a GIS-Based Bivariate Statistical Models and Machine Learning in the Dodota Alem Watershed, Ethiopia
Gizaw Tesfaye*
,
Daniel Bekele,
Melat Eshetu,
Mohamed Rabo,
Abebe Bezu,
Abera Asefa
Issue:
Volume 8, Issue 3, September 2024
Pages:
49-64
Received:
12 August 2024
Accepted:
4 September 2024
Published:
23 September 2024
Abstract: One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed of the Awash River basin, covering 135 km². Our modeling utilized input data from field surveys, Google Earth, and secondary sources. Geo-environmental factors such as land use and land cover, soil characteristics, altitude, slope, aspect, profile curvature, plan curvature, drainage density, distance from roads, distance from streams, stream power index (SPI), and topographic wetness index (TWI) were considered after a multi-collinearity test. Among these factors, distance from roads had the most substantial impact on gully erosion susceptibility according to the RF model, while SPI played a crucial role in the FR and IoE models. Approximately 60% of the watershed falls into the moderate or high susceptibility category for gully erosion using the FR and IoE models, whereas the RF model projected the largest area in the very high susceptibility class. Validation results, based on the Area Under Curve (AUC), demonstrated prediction efficiencies of 0.912 (FR), 0.880 (IoE), and 0.932 (RF). These findings can guide decision-makers and planners in implementing effective soil and water conservation measures to mitigate the damage caused by gully erosion. Additionally, this approach serves as a valuable reference for future research on gully erosion susceptibility.
Abstract: One of the most significant environmental hazards threatening ecosystems is gully erosion. In this study, we applied two bivariate statistical models—frequency ratio (FR) and index of entropy (IoE)—as well as a machine learning algorithm (RF) to generate gully erosion susceptibility maps (GESM). The study was conducted in the Dodota Alem watershed ...
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Research Article
Land Use Land Cover Change and Its Effect on Selected Soil Physico-Chemical Properties in Southwest Ethiopia
Issue:
Volume 8, Issue 3, September 2024
Pages:
65-78
Received:
17 August 2024
Accepted:
9 September 2024
Published:
26 September 2024
Abstract: Land cover transformation exerts adverse effects on the environment. This study examined the changes in land cover in the Semen Bench District of southwest Ethiopia from 1986 to 2018, as well as its implications for soil physico-chemical properties. A mixed-method approach was employed, integrating remote sensing (RS) and geospatial data with soil physico-chemical analysis and key informant interviews. Landsat images were processed using ERDAS IMAGINE 2015, and the land use land cover (LU/LC) map was classified using a supervised method employing the maximum likelihood classifier (MLC) algorithm. The classification accuracy was 90%, 87.5%, and 90% for the years 1986, 2001, and 2018, respectively, with corresponding kappa coefficients of 0.87, 0.83, and 0.87. One-way analysis of variance (ANOVA) was conducted to assess differences in soil parameters across various land uses, utilizing SAS software (Version 9.3). The findings indicated that agroforestry and settlements increased by 95% and 428.7%, respectively, while forestland and cropland decreased by 38.6% and 96%, respectively, primarily driven by the expansion of cash crops such as coffee, khat, and eucalyptus, as well as population growth. Significant changes (P<0.05) were observed in soil bulk density, soil organic matter, soil pH, available phosphorus, total nitrogen, exchangeable cations, cation exchange capacity, and electrical conductivity, due to land cover change. Conversely, soil texture remained unaffected (P>0.05) by these transformations. Consequently, it is essential to develop sustainable natural resource management plans to combat deforestation and the decline in soil fertility.
Abstract: Land cover transformation exerts adverse effects on the environment. This study examined the changes in land cover in the Semen Bench District of southwest Ethiopia from 1986 to 2018, as well as its implications for soil physico-chemical properties. A mixed-method approach was employed, integrating remote sensing (RS) and geospatial data with soil ...
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Research Article
Evaluating the Performance of a Stacking-Based Ensemble Model for Daily Temperature Prediction
Issue:
Volume 8, Issue 3, September 2024
Pages:
79-85
Received:
21 August 2024
Accepted:
23 September 2024
Published:
29 September 2024
Abstract: Temperature, as a critical element of weather forecasting, has consistently attracted extensive public attention. Accurate daily temperature prediction is essential for mitigating economic losses, preventing casualties, and maintaining public safety. However, traditional temperature prediction methods often fail to forecast the temperature promptly and effectively. To achieve more accurate daily temperatures prediction, researchers have turned to the recent advancement of artificial intelligence. This study aims to address the prediction of daily temperature in Algiers, by developing a stacking-based ensemble model. Firstly, the data normalization method is employed to preprocess the raw temperature data of Algiers in the experiment. Secondly, Decision Tree, K-Nearest Neighbors, Linear Regression, Random Forest, Recurrent Neural Network, and Support Vector Regression are selected as base models to predict the daily temperature. Finally, a stacking-based ensemble model with Recurrent Neural Network as the meta regressor (S-RNN) is applied for further accurate prediction. The experiment involves evaluating multiple metrics on the dataset to assess the performance of the model in predicting daily temperatures in Algiers. The experimental results indicate that the ensemble model outperforms other base models in addressing the challenges of daily temperature prediction. Meanwhile, this study confirms the significant potential in the application of stacking-based ensemble learning in the field of daily temperature prediction.
Abstract: Temperature, as a critical element of weather forecasting, has consistently attracted extensive public attention. Accurate daily temperature prediction is essential for mitigating economic losses, preventing casualties, and maintaining public safety. However, traditional temperature prediction methods often fail to forecast the temperature promptly...
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