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首页> 外文期刊>Journal of Environmental Management >Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool
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Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool

机译：使用神经模糊建模工具对流域本地流量进行分析和预测

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摘要

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.
机译：传统上，多元线性回归技术一直是模拟水文时间序列中使用最广泛的模型之一。但是，当非线性现象很明显时，多重线性将无法建立适当的预测模型。近年来，神经模糊系统已经在校准非线性关系方面获得了广泛的应用。这项研究评估了神经模糊系统作为传统统计回归技术的替代方法的潜力，目的是预测流域本地来源的流量。通过对印度尼西亚Citarum河的河水时间序列的模拟研究，证明了所提出的识别技术的有效性。此外，为了提供与河流流量估算有关的不确定性，进行了蒙特卡洛模拟。作为比较，还使用各种统计指标检查了西塔鲁姆河管理局正在使用的多元线性回归分析。使用95％置信区间的模拟结果表明，神经模糊模型始终低估了高流量的大小，而低流量和中流量的大小则估计得更接近观察到的数据。神经模糊和线性回归方法的预测准确性的比较表明，神经模糊方法在预测河流水流动力学方面更准确。神经模糊模型能够将多元线性回归预测的均方根误差（RMSE）和平均绝对百分比误差（MAPE）值分别提高约13.52％和10.73％。考虑到其简单性和效率，建议将神经模糊模型作为在研究区域中对流动动力学建模的替代工具。

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