Department of Agricultural and Bioresources Engineering, Nnamdi Azikiwe University, Awka, Nigeria.
National Centre for Agricultural Mechanization, Ilorin, Nigeria.
Sci Rep. 2021 Dec 24;11(1):24438. doi: 10.1038/s41598-021-04062-5.
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 R 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 R 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 R 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.
本研究旨在分析阿南布拉州新威市 Ele 河的水质，以便使用人工神经网络 (ANN) 预测一年的水质指数。水污染已构成重大问题，识别河流系统中的污染点是一项非常困难的任务。为了克服这一任务，需要通过建模和预测四个不同地点的四个水质参数（pH 值、总溶解固体 (TDS)、电导率 (EC) 和钠 (Na)）来确定污染水平。水质结果表明，在雨季和旱季，所有四个地点的 pH 值范围在 6.01 至 6.87 之间，均在粮农组织标准范围内；而 TDS（mg/l）、EC（dS/m）和 Na（mg/l）参数值范围在 2001 至 2506、3.01 至 5.76 和 40.42 至 73.45 之间，均高于粮农组织标准的 1 号至 3 号点，而在 4 号点，TDS、EC 和 Na 值范围在 1003 至 1994、2.01 至 2.78 和 31.24 至 39.44 之间，均在粮农组织标准范围内。然而，在旱季，TDS、EC 和 Na 值范围在 2002 至 2742、3.04 至 5.82 和 40.14 至 88.45 之间，均高于粮农组织标准。总的来说，人工神经网络很好地模拟了实际的水质数据集，具有良好的预测能力。训练模型性能评估显示，pH 值、TDS、EC 和 Na 的 R 值范围分别为 0.981 至 0.990、0.981 至 0.988、0.981 至 0.989 和 0.981 至 0.989。测试模型性能显示，pH 值、TDS、EC 和 Na 的 R 值范围分别为 0.952 至 0.967、0.953 至 0.970、0.951 至 0.967 和 0.953 至 0.968。预测性能评估显示，pH 值、TDS、EC 和 Na 的 R 值范围分别为 0.945 至 0.968、0.946 至 0.968、0.944 至 0.967 和 0.949 至 0.965。还观察到，pH 值、TDS、EC 和 Na 的均方根误差 (RMSE) 范围分别为 0.022 至 0.088、0.012 至 0.087、0.015 至 0.085 和 0.014 至 0.084。这项研究的信息将为研究人员提供灌溉用水质指数方面的指导。此外，它还将为政府和机构在水资源政策、管理和决策方面提供指导。
