Relative Performance of Artificial Neural Networks and Regression Models in Predicting Missing Water Quality Data

Tyagi, Punam; Chandra-Mouli, Venkatraman; Lingireddy, Srinivasa; Buddhi, D.

doi:10.1089/ees.2007.0045

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…In practice, for ANN, it will be more desirable to split the data into training, validation, and testing. However, due to limited data in the present study, the data will only be segregated into two parts [20].…”

Section: Data Partitioning and Selected Water Parametersmentioning

confidence: 99%

Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir

Ieong

Lou

Ung

et al. 2014

Environ Model Assess

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Principle component regression (PCR), artificial neural network (ANN), and their combination used as datadriven models were selected to apply in this study to predict (based on the current-month variables) and forecast (based on the last 3-month-ahead variables) the phytoplankton dynamics in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. The models used the comprehensive 8 years' monthly water quality data for training and the most recent 3 years' monthly data for testing. Twenty-four water quality variables including physical, chemical, and biological parameters were involved, and comparisons were made to select the best models that can be applied to MSR. Simulation results revealed that ANN has better accuracy and generalization performance in comparison with PCR both for the prediction and the forecasted model. Using principal component analysis (PCA) for the data, inputs did not show better performance for the ANN, implying that eliminating the uncorrelated variables do not increase the prediction capability for the adopted model. Globally, in contrast with previous studies showing that the hybrid model can handle both linear and nonlinear components of the problems well, the PCR-ANN in this study obtain no better improvement.

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Section: Data Partitioning and Selected Water Parametersmentioning

confidence: 99%

Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir

Ieong

Lou

Ung

et al. 2014

Environ Model Assess

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show abstract

“…Both ANNs and fuzzy theories are state-of-the-art technologies that try to mimic the human thinking process for learning similar strategies or experiences to make optimal decisions, and are well recognized for their outstanding abilities in modeling complex nonlinear systems such as precipitation estimation/prediction (Chiang et al, 2007), streamflow forecasting (Abrahart and See, 2002;Brath et al, 2002;Chiang et al, 2004;Dawson et al, 2002;Shrestha and Nestmann, 2009;Toth, 2009), reservoir operations (Chaves and Kojiri, 2007;Hsu and Wei, 2007;Mehta and Jain, 2009;Pinthong et al, 2009), prediction of water quality parameters (Sudheer et al, 2006;Tyagi et al, 2008), and pumping operations (Chang et al, 2008;Rao et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

Chiang

Chang

Tsai

et al. 2011

Hydrol. Earth Syst. Sci.

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Abstract.Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS) and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

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Comparison of prediction methods for oxygen-18 isotope composition in shallow groundwater

Cerar

Mezga

Žibret

et al. 2018

Science of The Total Environment

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Relative Performance of Artificial Neural Networks and Regression Models in Predicting Missing Water Quality Data

Cited by 6 publications

References 36 publications

Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir

Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

Comparison of prediction methods for oxygen-18 isotope composition in shallow groundwater

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