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

Medium: journal article
Language(s): en 
Published in: Civil Engineering Journal, , n. 1, v. 4
Page(s): 188
DOI: 10.28991/cej-030978

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.

Copyright: © 2018 Ehsan Jafari Nodoushan,

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

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