A comprehensive study on predicting river runoff

Tran, Thi Thu Thuy; Giang, Ngo Ngoc Hoang; Duong, Hieu N.; Nguyen, Hien; Hoài, Trần Văn; Nghi, Vu Van

doi:10.1109/kse.2017.8119467

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The approach works on the assumption that variables of importance with respect to different time lags can be identified by analyzing cross correlation, ACF and partial autocorrelation (PACF) between the variables under consideration. Detailed explanation of PACF in runoff estimation can be found in [34]. In this experiment, we have used PACF to analyze our input dataset.…”

Section: Selection Of Inputs To the Modelmentioning

confidence: 99%

Runoff estimation using modified adaptive neuro-fuzzy inference system

Nath

Mthethwa

Saha

2019

Environmental Engineering Research

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Rainfall-Runoff modeling plays a crucial role in various aspects of water resource management. It helps significantly in resolving the issues related to flood control, protection of agricultural lands, etc. Various Machine learning and statistical-based algorithms have been used for this purpose. These techniques resulted in outcomes with an acceptable rate of success. One of the pertinent machine learning algorithms namely Adaptive Neuro Fuzzy Inference System (ANFIS) has been reported to be a very effective tool for the purpose. However, the computational complexity of ANFIS is a major hindrance in its application. In this paper, we resolved this problem of ANFIS by incorporating one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS. The results of the modified ANFIS were found to be satisfactory. The performance of this modified ANFIS is then compared with conventional ANFIS and another popular statistical modeling technique namely ARIMA model with respect to the forecasting of runoff. In the present investigation, it was found that proposed PSO-ANFIS performed better than ARIMA and conventional ANFIS with respect to the prediction accuracy of runoff.

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Section: Selection Of Inputs To the Modelmentioning

confidence: 99%