Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean

Alizadeh, Mohamad Javad; Kavianpour, Mohammad Reza

doi:10.1016/j.marpolbul.2015.06.052

Cited by 136 publications

(49 citation statements)

References 20 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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]. In this study, performance of ANN models and wavelet-ANN models for water quality forecasting have been investigated in which the results indicated outperformance of wavelet-ANN models compared with the ANN models.…”

Section: Introductionmentioning

confidence: 92%

“…Regarding water quality in coastal and estuarine waters, a wide variety of parameters can be influential which finding an exact relationship among them is not an easy task. Traditionally, the methods applied for water quality modelling and forecasting were based on linear relationships which mainly were not accurate enough due to ignoring nonlinear relationships among the variables [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Nodoushan

2018

CivileJournal

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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

Nodoushan

2018

CivileJournal

View full text Add to dashboard Cite

show abstract

“…In this study, the hidden layer nodes for the model are gradually increased from 5 to 250 with the interval 5. In addition, the model forecasts the values of the COD and BOD concentrations at time t using the three principal components in the input structure with different time lags (up to seven prior days) [58].…”

Section: Assessing the Performance Of The Forecasting Modelmentioning

confidence: 99%

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Yang

Bai

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:The stable operation of sewage treatment is of great significance to controlling regional water environment pollution. It is also important to forecast the inlet water quality accurately, which may ensure the purification efficiency of sewage treatment at a low cost. In this paper, a combined kernel principal component analysis (KPCA) and extreme learning machine (ELM) model is established to forecast the inlet water quality of sewage treatment. Specifically, KPCA is employed for feature extraction and dimensionality reduction of the inlet wastewater quality and ELM is utilized for the future inlet water quality forecasting. The experimental results indicated that the KPCA-ELM model has a higher accuracy than the other comparison PCA-ELM model, ELM model, and back propagation neural network (BPNN) model for forecasting COD and BOD concentration of the inlet wastewater, with mean absolute error (MAE) values of 2.322 mg/L and 1.125 mg/L, mean absolute percentage error (MAPE) values of 1.223% and 1.321%, and root mean square error (RMSE) values of 3.108 and 1.340, respectively. It is recommended from this research that the method may provide a reliable and effective reference for forecasting the water quality of sewage treatment.

show abstract

“…WNN is a combination of wavelet analysis theory and neural network theory, (18) which takes the wavelet base function as the transfer function of the hidden layer node, and the neural network with the error of the signal forward propagation. In Fig.…”

Section: Wnnmentioning

confidence: 99%

Assessment of Geological Hazards in Ningde Based on Hybrid Intelligent Algorithm

Zhang¹,

Wang²,

Chang³

2018

Sensors and Materials

View full text Add to dashboard Cite

Keywords: geological hazard, neural network, hazards assessment, WNNIn recent years, geological hazards have occasionally occurred throughout the world, and have caused immense damage to roads and lives in places where landslides have occurred. Thus, it is of great importance to coordinate the work of regional hazard prevention and reduction. Under these circumstances, hybrid intelligent algorithm (HIA) combined with genetic algorithm (GA) and wavelet neural network (WNN) is proposed with the geological risk assessment analysis in our study. The HIA integrated both the geographic information system (GIS) technology and the artificial neural network model. In the HIA, GA is adopted to initialize the network connection weights and thresholds of WNN. Moreover, in simulations, measurement-obtained data of geological hazards were collected by a geological environment monitoring station and statistic data which were extracted from the map and statistics text data from Ningde City in eastern China. The proposed HIA provides us with increased accuracy compared with established methods using traditional back propagation (BP) neural networks. Our result is of great importance for regional geological hazard prevention, land resources rational development, and proper protection of the geological environment.

show abstract

Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean

Cited by 136 publications

References 20 publications

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

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

Inlet Water Quality Forecasting of Wastewater Treatment Based on Kernel Principal Component Analysis and an Extreme Learning Machine

Assessment of Geological Hazards in Ningde Based on Hybrid Intelligent Algorithm

Contact Info

Product

Resources

About