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Predicting the amount of daily rainfall improves agricultural productivity and secures food and water supply to keep citizens healthy. To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets. An erratic rainfall distribution in the country affects the agriculture on which the economy of the country depends on. Wise use of rainfall water should be planned and practiced in the country to minimize the problem of the drought and flood occurred in the country. The main objective of this study is to identify the relevant atmospheric features that cause rainfall and predict the intensity of daily rainfall using machine learning techniques. The Pearson correlation technique was used to select relevant environmental variables which were used as an input for the machine learning model. The dataset was collected from the local meteorological office at Bahir Dar City, Ethiopia to measure the performance of three machine learning techniques (Multivariate Linear Regression, Random Forest, and Extreme Gradient Boost). Root mean squared error and Mean absolute Error methods were used to measure the performance of the machine learning model. The result of the study revealed that the Extreme Gradient Boosting machine learning algorithm performed better than others.

Research results published regarding the impact of soil and water conservation practices in the highland areas of Ethiopia have been inconsistent and scattered. In this paper, a detailed review and synthesis is reported that was conducted to identify the impacts of soil and water conservation practices on crop yield, surface run-off, soil loss, nutrient loss, and the economic viability, as well as to discuss the implications for an integrated approach and ecosystem services. The review and synthesis showed that most physical soil and water conservation practices such as soil bunds and stone bunds were very effective in reducing run-off, soil erosion and nutrient depletion. Despite these positive impacts on these services, the impact of physical soil and water conservation practices on crop yield was negative mainly due to the reduction of effective cultivable area by soil/stone bunds. In contrast, most agronomic soil and water conservation practices increase crop yield and reduce run-off and soil losses. This implies that integrating physical soil and water conservation practices with agronomic soil and water conservation practices are essential to increase both provisioning and regulating ecosystem services. Additionally, effective use of unutilized land (the area occupied by bunds) by planting multipurpose grasses and trees on the bunds may offset the yield lost due to a reduction in planting area. If high value grasses and trees can be grown on this land, farmers can harvest fodder for animals or fuel wood, both in scarce supply in Ethiopia. Growing of these grasses and trees can also help the stability of the bunds and reduce maintenance cost. Economic feasibility analysis also showed that, soil and water conservation practices became economically more viable if physical and agronomic soil and water conservation practices are integrated.

Global climate change is affecting water resources and other aspects of life in many countries. Rainfall is the most significant climate element affecting the livelihood and well-being of the majority of Ethiopians. Rainfall variability has a great impact on agricultural production, water supply, transportation, the environment, and urban planning. Because all agricultural activities and subsequent national crop production hinge on the amount and distribution of rainfall, accurate monthly and seasonal predictions of this rainfall are vital for agricultural planning. Rainfall prediction is also useful for governmental, non-governmental, and private agencies in making long-term decisions and planning in numerous areas such as farming, early warning of potential hazards, drought mitigation, disaster prevention, and insurance policy. Artificial Intelligence (AI) has been widely used in almost every area, and rainfall prediction is one of them. In this study, we attempt to investigate the use of AI-based models to predict monthly rainfall at 92 Ethiopian meteorological stations. The applicability of Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting long-term monthly precipitation was investigated using geographical and periodicity component (longitude, latitude, and altitude) data collected from 2011 to 2021. The experimental results reveal that the ANFIS model outperforms the ANN model in all assessment criteria across all testing stations. The Nash–Sutcliffe efficiency coefficients were 0.995 for ANFIS and 0.935 for ANN over testing stations.

The Hulan River Basin is located in the black soil region of northeast China. This region is an important food-producing area and the susceptibility of black soil to erosion increases the risk of soil erosion, which is a serious environmental problem that affects agricultural productivity, water supply, and other important aspects of the region. In this paper, the changes in LULC (land use and land cover) in the basin between 2001 and 2020 were thoroughly analysed using GIS (geographic information system) and USLE (universal soil loss equation) models. The soil erosion risk in the Hulan River Basin between 2001 and 2020 was also studied and soil erosion hot spots were identified to target those that remained significant even under the implementation of soil conservation measures. Precipitation data were used to obtain the R factor distribution, LULC classification was adopted to assess the C factor distribution, soil data were employed to estimate the K factor distribution, DEM (Digital Elevation Model) data were used to generate an LS factor map, and slope and LULC data were considered to produce a P factor distribution map. These factors were based on the model parameters of the USLE. The findings of LULC change analysis over the last 20 years indicated that, while there have been nonobvious changes, agricultural land has continued to occupy the bulk of the area in the Hulan River Basin. The increase in areas used for human activities was the most notable trend. In 2001, the model-predicted soil erosion rate varied between 0 and 120 t/ha/yr, with an average of 4.63 t/ha/yr. By 2020, the estimated soil erosion rate varied between 0 and 193 t/ha/yr, with an average of 7.34 t/ha/yr. The Hulan River Basin was classified into five soil erosion risk categories. Most categories encompassed extremely low-risk levels and, over the past 20 years, the northeastern hilly regions of the basin have experienced the highest concentration of risk change areas. The northeastern hilly and mountainous regions comprised the risk change area and the regions that are most susceptible to erosion exhibited a high concentration of human production activities. In fact, the combined use of GIS and USLE modelling yielded erosion risk areas for mapping risk classes; these results could further assist local governments in improving soil conservation efforts.

Monitoring water quality and quantity is crucial to be sure that water resources are sustainably used. However, there is no monitoring system of water quantity and quality in southwestern Ethiopia, despite expansion of agricultural activities demanding water resources. The objective of this study was to investigate the effect of agriculture on water quantity and quality with special emphasis on irrigation in southwestern Ethiopia. Data of water quantity was collected from four rivers and four irrigation canals during dry season of 2023. Physico-chemical water quality data was collected from 35 sites. Water quantity was calculated by estimating the water discharge of the rivers and irrigation canals. Weighted arithmetic water quality index was calculated to assess the status of the studied rivers. Principal component analysis was used to identify the relation of the sites with water quality parameters. This study revealed that the average amount of abstracted water for irrigation from the four studied rivers was 22,399 m 3 /day during the studied period, and the average percentage of abstracted water was 17%. Sites downstream of the irrigation site were characterized by poor water quality compared with the upstream sites. Sites surrounded by agricultural land use were correlated with chemical oxygen demand, electric conductivity, nitrate, orthophosphate, water temperature, and pH, whereas all sites surrounded by forest were positively correlated with dissolved oxygen. This study indicates that agricultural activities have a negative impact on surface water quality and quantity if not managed properly. Hence, we recommend sustainable use of water resources for the planned irrigation expansion.

Present study was conducted in the Northern Ethiopia with the aim to identify and map potential groundwater sites using the state art and science of geo-spatial technology techniques. This method used to prepare the spatial factors derived from various sources including satellite images, existing thematic maps and to develop the model. The data employed include climatic and biophysical data like rainfall, geology, soil and land use land cover. Computation of the parameters weight impact was calculated and accordingly, geology, lineament density and geomorphology were found the most determinant factors influencing groundwater potential (GWP) occurrence; whereas, land use land cover, soil and rainfall were perceived the least significant elements. The final GWP map was developed through the integration of the selected eight variables into a system of weight overlay modeling with the ArcGIS interface environment. The result GWP map was generated in to five suitability classes as very good (14%), good (16%), moderate (24%), and the rest (47%) is under poor and very poor classes respectively. Very good potential sites were geographically lied in the plateau, flat to gentle sloppy, while areas under good classes were situated on moderately sloppy of Alaje formation under undulated surface of the study area. Similarly, the moderate potential areas were found in undulated surface of Alaje and Ashengie formations. In contrast, the poor and very poor GWP areas was lied beneath Aiba and small amount in Alaje formation in the steeply slope. Generally, the spatial distribution of the groundwater occurrence and movement in the area is mainly regulated by geology, geomorphology and lineament density. Finally, the performance of the groundwater potential of the model was cross validated by area under curve of (AUC) of receiver operating characteristic (ROC) and was found 85% accurate. Furthermore studies are demanded considering high quality data supported with intensive field measurement for better accuracy and outcome.

Background Besides their benefits, heavy metals are toxic, persistent, and hazardous to human health, even at their lower concentrations. Consumption of unsafe concentrations of food contaminated with heavy metals may lead to the disruption of numerous biological and biochemical processes in the human body. In developing country including Ethiopia, where untreated or partially treated wastewater is used for agricultural purposes, the problems related to the consumption foods contaminated with heavy metals may poses highest risk to human health. Therefore, this review was aimed to determine the public health implications of heavy metals in foods and drinking water in Ethiopia. Methods The articles published from 2016 to 2020 were identified through systematic searches of electronic databases that include MEDLINE/PubMed, EMBASE, CINAH, Google Scholar, WHO, and FAO Libraries. The data was extracted using a predetermined data extraction form using Microsoft Excel, 2016. The methodological quality of the included studies was assessed using mixed methods appraisal tool (MMAT) version 2018 and Joanna Briggs Institute Critical Appraisal tools to determine the relevance of the studies. Finally, the results were evaluated based on the FAO/WHO guidelines for foods and drinking water. Results A total of 1019 articles published from 2016 to 2020 were searched from various electronic databases and by manual searching on Google. Following the initial screening, 317 articles were retrieved for evaluation and 49 articles were assessed for eligibility, of which 21 studies were included in the systematic review. The mean concentration of Cr, Cd, Pb, As, Hg, Zn, Cu, Ni, Co, Fe and Mn in fruits and vegetables ranged from 2.068–4.29, 0.86–1.37, 1.90–4.70, 1.01–3.56, 3.43–4.23, 19.18–98.15, 4.39–9.42, 1.037–5.27, 0.19–1.0, 199.5–370.4, 0.26–869 mg/kg, respectively. The mean concentration Cr, Cd, Pb, As, Zn, and Fe in meat and milk ranged from 1.032–2.72, 0.233–0.72, 1.32–3.15, 0.79–2.96, 78.37–467.7, and 505.61–3549.9 mg/kg, respectively. The mean concentration of Cr, Cd, Pb, Zn, and Cu in drinking water ranged 0.0089–0.054, 0.02–0.0237, 0.005–0.369, 0.625–2.137, and 0.176–1.176 ml/L, respectively. The mean concentration of Cr, Cd, Pb, Zn, Cu, Ni, Co, Fe, and Mn in other edible cereals ranged from 0.973–2.165, 0.424–0.55, 0.65–1.70, 70.51–81.58, 14.123–15.98, 1.89–13.8, 1.06–1.59, 67.866–110.3, and 13.686–15.4 mg/kg, respectively. Conclusion This systematic review identified heavy metals in foods and drinking water and determined their public health implications. The results of this finding imply that the majority of the studies reported high concentrations of toxic heavy metals in foods and drinking water that are hazardous to human health. Therefore, effective food safety and risk-based food quality assessment are essential to protect the public health.

Climate change is likely to have severe effects on water availability in Ethiopia. The aim of the present study was to assess the impact of climate change on the Gilgel Abay River, Upper Blue Nile Basin. The Statistical Downscaling Tool (SDSM) was used to downscale the HadCM3 (Hadley centre Climate Model 3) Global Circulation Model (GCM) scenario data into finer scale resolution. The Soil and Water Assessment Tool (SWAT) was set up, calibrated, and validated. SDSM downscaled climate outputs were used as an input to the SWAT model. The climate projection analysis was done by dividing the period 2010-2100 into three time windows with each 30 years of data. The period 1990-2001 was taken as the baseline period against which comparison was made. Results showed that annual mean precipitation may decrease in the first 30-year period but increase in the following two 30-year periods. The decrease in mean monthly precipitation may be as much as about -30% during 2010-2040 but the increase may be more than +30% in 2070-2100. The impact of climate change may cause a decrease in mean monthly flow volume between -40% to -50% during 2010-2040 but may increase by more than the double during 2070-2100. Climate change appears to have negligible effect on low flow conditions of the river. Seasonal mean flow volume, however, may increase by more than the double and +30% to +40% for the Belg (small rainy season) and Kiremit (main rainy season) periods, respectively. Overall, it appears that climate change will result in an annual increase in flow volume for the Gilgel Abay River. The increase in flow is likely to have considerable importance for local small scale irrigation activities. Moreover, it will help harnessing a significant amount of water for ongoing dam projects in the Gilgel Abay River Basin.