Abstract: Degradation of fragile lands and gully formation are pressing challenges in Western Ethiopia, particularly in the Mana Sibu District. The integration of Chomo grass (Brachiaria humidicola), a stoloniferous perennial grass with strong adaptive and restorative properties, has shown promising potential for ecological restoration and soil conservation. This study aimed to characterize the morphological traits of Chomo grass across different age categories and assess its role in the sustainable rehabilitation of degraded landscapes. A randomized complete block design was employed to evaluate both above- and below-ground morphological traits, including plant height, stolon length, leaf sheath, root depth, and plant density. Results revealed statistically significant differences (p<0.001) in most traits across age groups, indicating rapid early development and increasing restoration capacity with plant age. The highest ground cover (98.67%) and root length (125 cm) were recorded in older stands, supporting its effectiveness in enhancing soil stability, vegetation recovery, and water retention. Field observations further confirmed Chomo grass’s role in stabilizing gullies and fragile lands, reducing erosion, and supporting livelihoods through fodder production. The study recommends the expansion of Chomo grass as a viable biological soil and water conservation strategy in degraded areas.
Abstract: Degradation of fragile lands and gully formation are pressing challenges in Western Ethiopia, particularly in the Mana Sibu District. The integration of Chomo grass (Brachiaria humidicola), a stoloniferous perennial grass with strong adaptive and restorative properties, has shown promising potential for ecological restoration and soil conservation....Show More
Abstract: Short-term load forecasting plays an important and indispensable role in the daily operation planning of power grid because it allows grid operators to predict electricity demand a few hours to one week in advance. Although statistics-based methods and machine learning-based methods have been widely used in short-term load forecasting, a single model may have difficulty capturing all underlying dynamics, causing reduced prediction accuracy. Therefore, a stacking-based ensemble model that improves prediction accuracy by integrating multiple base prediction models is proposed in this study for short-term load forecasting. Firstly, for data preprocessing, data normalization is used to scale the raw load data to a range of 0 to 1. Data imputation is used to ensure data integrity. Secondly, base prediction models including logistic regression, decision tree, random forest, multilayer perceptron, convolutional neural network, and long short-term memory are utilized to train the prediction models. Thirdly, the stacking-based ensemble learning method is utilized to integrate these base prediction models to further predict electric load. The results of comparative experiments and error analysis show that the stacking-based ensemble learning model outperforms the base prediction models for the majority of the evaluation metrics. Additionally, the analysis of curve fitting results demonstrates the high level of agreement between the actual values and the predicted values for the stacking-based ensemble learning model.
Abstract: Short-term load forecasting plays an important and indispensable role in the daily operation planning of power grid because it allows grid operators to predict electricity demand a few hours to one week in advance. Although statistics-based methods and machine learning-based methods have been widely used in short-term load forecasting, a single mod...Show More