Rainfall-Riverflow Trends of Enyong Creek in Akwa Ibom State, Nigeria

C. U. Augustine, I. Ahaneku, J. Awu
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Abstract

Rainfall-riverflow is crucial for effective hydrology and water resource management. Hence, the objective of this study was to evaluate the rainfall-riverflow trends of Enyong Creek in Akwa Ibom State, Nigeria, utilizing daily hydro-meteorological data of daily rainfall, river discharge, and temperature data collected from the period 2018 to 2023 and modeling the data by Vector Autoregressive (VAR) models. The results show that the VAR model successfully captured the dynamic relationships among water discharge (WD), rainfall (RF), and average temperature (AVE.TEMP). Equations revealed the influence of past values on the current state of each variable. Correlation matrix and graphical representations confirmed model adequacy. Validation results demonstrated the model's accuracy, with model R-squared value of 0.8781 indicating a strong correlation. The performance measurement of evaluation for the developed model showed a Mean Average Error (MAE), Root Mean Square error (RMSE), and Mean Absolute Percentage Error (MAPE) values of 5.5066, 6.7831, and 7.4203 respectively, revealing a satisfactory accuracy and precision. Information derived from this study offers valuable insights for government officials, policymakers, and planners in accurate flood forecasting, emergency management, land use planning, and infrastructure development.
尼日利亚阿夸伊博姆州 Enyong 溪的降雨-河流趋势
降雨-河水流量对于有效的水文和水资源管理至关重要。因此,本研究旨在评估尼日利亚阿夸伊博姆州 Enyong 溪的降雨-河水流量趋势,利用从 2018 年至 2023 年期间收集的每日降雨量、河流排水量和温度数据等水文气象数据,并通过矢量自回归(VAR)模型对数据进行建模。结果表明,VAR 模型成功捕捉了排水量(WD)、降雨量(RF)和平均气温(AVE.TEMP)之间的动态关系。方程揭示了过去的数值对每个变量当前状态的影响。相关矩阵和图形表示法证实了模型的适当性。验证结果表明了模型的准确性,模型 R 平方值为 0.8781,表明模型具有很强的相关性。对所开发模型的性能测量评估显示,平均误差(MAE)、均方根误差(RMSE)和平均绝对误差(MAPE)值分别为 5.5066、6.7831 和 7.4203,显示了令人满意的准确性和精确度。这项研究得出的信息为政府官员、决策者和规划者提供了准确的洪水预报、应急管理、土地利用规划和基础设施开发方面的宝贵见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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