This study was aimed at analyzing the water quality of Ele River Nnewi, Anambra State for irrigation purposes with a view to predicting a one-year water quality index using Artificial Neural Network (ANN). Water pollution has posed a major problem and identifying the points of pollution in the River system is a very difficult task. To overcome this task, the need to determine the pollution level arose by modeling and predicting four water quality parameters at four (4) different locations using the Artificial Neural Network. These parameters include the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Sodium (Na), respectively. The water quality results showed that the pH values which ranges from 6.01 to 6.87 were within the FAO standard in all the points for both rainy and dry seasons, whereas the TDS (mg/l), EC (dS/m) and Na (mg/l) parametric values range from 2001 to 2506, 3.01 to 5.76, and 40.42 to 73.45 respectively, were above the FAO standard from point 1 to point 3 and falls within the FAO standard at point 4 with values ranging from 1003 to 1994, 2.01 to 2.78 and 31.24 to 39.44, respectively. However, during the dry season, the TDS, EC, and Na values range from 2002 to 2742, 3.04 to 5.82 and 40.14 to 88.45 respectively, were all above the FAO standard. Generally, the artificial neural network modeled the actual water quality data set very well with good prediction. The training model performance evaluation shows that the R2 values ranges from 0.981 to 0.990, 0.981 to 0.988, 0.981 to 0.989 and 0981 to 0.989, for pH, TDS, EC, and Na. The testing model performance shows that the R2 value ranges from 0.952 to 0.967, 0.953 to 0.970, 0.951 to 0.967and 0.953 to 0.968, for pH, TDS, EC and Na while the forecast performance evaluation shows that the R2 values ranges from 0.945 to 0.968, 0.946 to 0.968, 0.944 to 0.967 and 0.949 to 0.965 for pH, TDS, EC and Na respectively. It was also observed that the Root Mean Squared Error (RMSE) ranges from 0.022 to 0.088, 0.012 to 0.087, 0.015 to 0.085 and 0.014 to 0.084 for pH, TDS, EC and Na, respectively. Information from this study will serve as a guide to researchers on the water quality index for irrigation purposes. Also, it will guide the government and agencies on policy, management and decision-making on water resources.
Flooding is a major environmental problem facing Anambra State of Nigeria, which also threatens food security in the state. To address this issue, continual flood vulnerability mapping exploring more efficient methods is needed to facilitate flood risk management in the state. The advantages of employing spatial information technologies such as Remote Sensing (RS) and Geographic Information System (GIS) in flood vulnerability mapping has been widely documented; the limitations of employing GIS alone in effective vulnerability analysis have also been documented by researchers. To overcome these limitations, this study adopted the use of GIS and the integration of Interval Value Fuzzy Rough Number (IVFRN), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Analytical Network Process (ANP) method in vulnerability assessment of flood hazard. The result of the study shows that the state is very vulnerable to flood with 73% of the total area of the state lying between Very High and Medium vulnerable zones. The most vulnerable Local Government Area (LGA) in the State is Anambra West with 95% of the total area of the LGA lying between Very High and Medium vulnerable zones. Furthermore, the obtained values ofR ÀD show that Rainfall Intensity factor is the major cause of flood in the study area with the highest positive value of 1.55 and Soil factor is the major effect with the highest negative value of -0.93. The IVFRN-DEMATEL-ANP assessment model was validated using AUC-ROC method; an AUC value of 0.946 was obtained, this indicates that the model has excellent prediction accuracy. This study was able to establish the feasibility of integrating the IVFRN, DEMATEL and ANP methods in flood vulnerability assessment. It is recommended that the provision of adequate drainage systems should be prioritized to areas of high flood vulnerability index; to help mitigate flood hazards in the State. Also, strategic planning of infrastructures and emergency routes for moving people and key assets from vulnerable areas especially during the rainy season should be geospatialbased and systematic.
Rice aggregation centers are tasked with checkmating substandard agricultural produce that are often encountered by the integrated millers during the course of buying from farm to farm to ensure already made market for their produce. Thus, it must be well placed to occupy strategic positions such that all different rice cultivating zones of the state get access to the facility. Given that these facilities will provide salient services, sets of demand points tasked with the provision storage, processing capability and a constant market for various rice farmers within the state. It is pertinent that these facilities are located properly considering all unique factors on ground. This study therefore aimed at a GIS-based multi criteria model for location of rice aggregation centers in Anambra State. The study was carried out using Geographical Information System (GIS) technology. Several GIS thematic layers were obtained and considered important factors in citing rice aggregation centers such as road network, Land Use and Land Cover (LULC), slope, river, cost distance, electricity network, floodplains, erosion plains and proximity to rice farms. It revealed optimal locations for siting a modular aggregation rice center at Nzam, Onoia, Aguleri, Nando, Akenu, Achalla, Ezira, Ndiokpalaeze, Ogbakuma and Uli. The goal throughout this study was to provide a reliable and complete analysis of siting modular rice aggregation centers in the agricultural zones in Anambra State. The approach and results obtained in this study are recommended as a spatial decision tool for site selection of modular rice aggregation centers in developing countries.
Excessive sediment deposition results to hydro-ecological problems particularly for shallow streams that experience significant point-source pollution. In recent times, models have been employed to investigate sediment transport in river systems. The aim of this research work is to model sediment transport of Ele River using particle tracing methodology. The governing equations of fluid flow and particle movement were modelled using COMSOL Multiphysics 5.3a. The result was validated using experimental data and the model result showed good agreement with coefficient of determination of 0.99. Study results showed that sediment at the river banks posses lower velocities compared to sediments in midstream. This implies higher sediment deposition at the banks due to low flow velocity. These sediments deposition constitute problems to the river system through degradation of water quality and blocking irrigation nozzles, impacting irrigation efficiency and crop production.
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