Bayesian Regularized Back-Propagation Neural Network Model for Chlorophyll-a Prediction: A Case Study in Meiliang Bay, Lake Taihu

GaoHailong,; QianXin,; ZhangRuibin,; YeRui,; LiuZhe,; Qian-yu,

doi:10.1089/ees.2015.0164

Cited by 14 publications

(1 citation statement)

References 32 publications

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“…In other study for the Stillaguamish River in Washington, Arya and Zhang (2015) employed predictive models using order series method (OSM) for investigating the normality assumption, water quality parameters such as temperature and dissolved oxygen have been taken under consideration [8]. Gao et al (2015) developed a Bayesian regularized back propagation ANN model for predicting monthly chlorophyll-a concentration in Meiliang Bay, Lake Taihu [9]. Alizadeh and Kavianpour (2015) developed wavelet-ANN models for daily forecasting of temperature, DO and turbidity in Hilo Bay, Pacific Ocean [1].…”

Section: Introductionmentioning

confidence: 99%

Monthly Forecasting of Water Quality Parameters within Bayesian Networks: A Case Study of Honolulu, Pacific Ocean

Nodoushan

2018

CivileJournal

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This study investigates the efficiency of Bayesian network (BN) and also artificial neural network models for predicting water quality parameters in Honolulu, Pacific Ocean. Monthly forecasting of three important characteristics of water body including water temperature, salinity and dissolved oxygen have been taken under consideration. Two separate strategies were applied in which the first strategy was related to prediction of the water quality parameters based on previous time series of the same variable. In the second strategy, an attempt was made to forecast DO using different affecting parameters such as temperature, salinity, previous time series of DO, and amount of chlorophyll. The efficiency of the models were assessed by using error measures. Results revealed that the BN models are superior over the ANN models in case of temperature and DO forecasting. Also, it was found that the first strategy is more efficient than the second strategy for predicting DO concentration. The best BN models for temperature, salinity and DO were achieved when time series of the same parameter up to 3, 2, and 3 previous months applied as input variables respectively. Overall, it can be concluded that BN and ANN models can be successfully applied for water quality modelling and forecasting in coastal waters. Moreover, the current study demonstrated that the BN models have a great ability dealing with time series including incomplete or missing data.

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