Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

Hipni, Afiq; El-Shafie, Ahmed; Najah, Ali; Karim, Othman A.; Hussain, Aini; Mukhlisin, Muhammad

doi:10.1007/s11269-013-0382-4

Cited by 162 publications

(64 citation statements)

References 33 publications

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“…Sivapragasam et al (2001) used SVR for forecasting rainfall and runoff. The SVR was successfully employed to forecast flood stage by Liong and Sivapragasam (2002), to develop rating curves at three gauging stations in Washington by Sivapragasam and Muttil (2005), to predict water level fluctuations of Lake Erie by Khan and Coulibaly (2006), to forecast long-term discharges by Lin et al (2006), to predict daily sediments in natural rivers by Cimen (2008), to model daily potential evapotranspiration by Kisi and Cimen (2009), to downscale the daily precipitations by Chen et al (2010), to predict daily streamflows with weather and climate inputs by Rasouli et al (2012), to forecast daily dam water levels by Hipni et al (2013), to improve forecast of annual rainfallrunoffs by Wang et al (2013), and to forecast daily river flows in the semiarid mountain region by He et al (2014). The major disadvantage of the SVR method is its higher computational burden for the constrained optimization problems.…”

mentioning

confidence: 99%

Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering

Kisi

2015

Water Resour Manage

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This paper investigates the ability of least square support vector regression (LSSVR) and adaptive neuro-fuzzy embedded fuzzy c-means clustering (ANFIS-FCM) in forecasting and estimation of monthly streamflows. In the first part of the study, the LSSVR and ANFIS-FCM models were tested in 1-month ahead streamflow forecasting by using crossvalidation method. Monthly streamflow data belonging to two stations, Besiri Station on Garzan Stream and Baykan Station on Bitlis Stream, in Dicle Basin of Turkey were used. The LSSVR and ANFIS-FCM results were compared with autoregressive moving average (ARMA) models. It was found that the LSSVR models performed better than the ANFIS-FCM and ARMA models in 1-month ahead streamflow forecasting. The ANFIS-FCM models are also found to be better than the ARMA models. The effect of periodicity on forecasting performance of the LSSVR models was also investigated. Adding periodicity component as input to the LSSVR models significantly improved the models' accuracy in forecasting. In the second part of the study, the accuracy of the LSSVR and ANFIS-FCM models was tested in streamflow estimation using data from nearby stream. Based on the results, the LSSVR was found to be better than the ANFIS-FCM and successfully used in estimating monthly streamflows by using nearby station data.

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confidence: 99%

Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering

Kisi

2015

Water Resour Manage

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“…V-fold cross-validation was selected to obtain an almost unbiased estimate of the algorithm performance but with a high variance because, in principle, it can be conducted for as long as one can afford to do so (indicating that it is a trial-and-error method to achieve optimal performances). The models exhibiting outstanding performances were observed when the V-fold was between 7 and 20 [45][46][47].…”

Section: Support Vector Machinementioning

confidence: 99%

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

et al. 2019

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The estimation of an increase in sea level with sufficient warning time is important in low-lying regions, especially in the east coast of Peninsular Malaysia (ECPM). This study primarily aims to investigate the validity and effectiveness of the support vector machine (SVM) and genetic programming (GP) models for predicting the monthly mean sea level variations and comparing their prediction accuracies in terms of the model performances. The input dataset was obtained from Kerteh, Tioman Island, and Tanjung Sedili in Malaysia from January 2007 to December 2017 to predict the sea levels for five different time periods (1, 5, 10, 20, and 40 years). Further, the SVM and GP models are subjected to preprocessing to obtain optimal performance. The tuning parameters are generalized for the optimal input designs (SVM2 and GP2), and the results denote that SVM2 outperforms GP with R of 0.81 and 0.86 during the training and testing periods, respectively, at the study locations. However, GP can provide values of 0.71 and 0.79 for training and testing, respectively, at the study locations. The results show precise predictions of the monthly mean sea level, denoting the promising potential of the used models for performing sea level data analysis.

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“…Hipni et al [17] point out that ANFIS has been widely used in the predictive modelling of problems related to hydrology, emphasising its ease of implementation, rapid and successful learning and a great generalization capacity, such as some of its causes popularity. Jain et al [18] use the neuronal networks to predict the input flow into the reservoir and Ondimu and Murase [19], for predictions of water level in reservoirs.…”

Section: State Of the Artmentioning

confidence: 99%

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

2019

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Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99.

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Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)

Cited by 162 publications

References 33 publications

Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering

Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering

Modeling the Nonlinearity of Sea Level Oscillations in the Malaysian Coastal Areas Using Machine Learning Algorithms

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

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