Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA

Heddam, Salim

doi:10.1080/09593330.2013.878396

Cited by 50 publications

(23 citation statements)

References 23 publications

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“…Artificial neural networks (ANNs) are an information processing system which belongs to the category of nonlinear models (Haykin 1999). ANNs are inspired from the function of the human brain.…”

Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Heddam

2016

Environ. Process.

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This study presents a new method called optimally pruned extreme learning machine (OP-ELM) for forecasting dissolved oxygen concentration (DO) several hours in advance. The forecast time horizon ranges from 24-h ahead (one day) to 168-h ahead (seven days). The proposed OP-ELM model is compared to the standard multilayer perceptron neural network (MLPNN) with respect to their capabilities of forecasting DO in the Klamath River at Miller Island Boat Ramp, Oregon, USA. To demonstrate the forecasting capability of OP-ELM and MLPNN models, we used a long-term data set of hourly DO data for a ten-year period, from 1 January 2004 to 31 December 2013, collected by the United States Geological Survey (USGS Stations No: 420,853,121,505,500 [Top] and 420,853,121,505,501 [Bottom]). For developing the models, we split the data set into a training subset (from 2004 to 2010) that corresponded to 70 %, and a validation (from 2011 to 2013) that corresponded to 30 % of the total data set. We investigated the performance and accuracy of the proposed two models for three different horizons, i.e., short-term, medium-term and long-term forecasting; a total of six different models (FM1 to FM6), having the same data sets as inputs, were developed for short-term (24 h to 48 h), medium-term (72 h to 96 h) and long-term (120 h to 168 h) horizons. Input variables used in the six models were the six antecedent DO concentrations at (t-5), (t-4), (t-3), (t-2), (t-1) and (t). The performance of the OP-ELM and MLPNN models in training and validation sets were compared with the observed data. To get more accurate evaluation of the results of the two models, the following seven statistical performance indices were used: the coefficient of correlation (R), the Willmott index of agreement (d), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), the mean absolute error (MAE), the bias error (Bias), and the mean absolute percentage error (MAPE). The study reveals that OP-ELM and MLPN provided good results and they were successful in forecasting DO at a high level of accuracy. The reliability of forecasting decreased Environ. Process. with increasing the step ahead. The measures of model performance fell within the acceptable ranges for the two stations. Regarding the fact that researches on medium and long-term forecasting are relatively limited, the present work aims to build and provide a good early warning system capable of preventing DO depletion and the associated problems of anoxia and hypoxia in river. Furthermore, the proposed forecasting models, when implemented appropriately, could be reliably used in detecting future change in DO concentration in rivers.

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Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Heddam

2016

Environ. Process.

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“…Singh et al [25] used water quality parameters to predict DO and biochemical oxygen demand in a river in India. Other studies (e.g., [26,27]) have used regression methods to predict DO in rivers using water temperature as inputs. In general, these studies have demonstrated that data-driven models can provide a suitable format for predicting DO with lower complexity [13].…”

Section: Data-driven Models and Annmentioning

confidence: 99%

Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis

Khan

Valeo

2017

Water

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Abstract:A fuzzy neural network method is proposed to predict minimum daily dissolved oxygen concentration in the Bow River, in Calgary, Canada. Owing to the highly complex and uncertain physical system, a data-driven and fuzzy number based approach is preferred over traditional approaches. The inputs to the model are abiotic factors, namely water temperature and flow rate. An approach to select the optimum architecture of the neural network is proposed. The total uncertainty of the system is captured in the fuzzy numbers weights and biases of the neural network. Model predictions are compared to the traditional, non-fuzzy approach, which shows that the proposed method captures more low DO events. Model output is then used to quantify the risk of low DO for different conditions.

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“…Other studies (e.g. Heddam, 2014;Ay and Kisi, 2011) have used regression to predict DO in rivers using water temperature or electrical conductivity, amongst others, as inputs. In general, these studies have demonstrated that there is a need and demand for less complex DO models, have led to an increase in the popularity of data-driven models (Antanasijević et al, 2014), and shown that the performance of these types of models is suitable.…”

Section: Fuzzy Numbers and Data-driven Modellingmentioning

confidence: 99%

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Khan

Valeo

2016

Hydrol. Earth Syst. Sci.

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Abstract.A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.

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Generalized regression neural network-based approach for modelling hourly dissolved oxygen concentration in the Upper Klamath River, Oregon, USA

Cited by 50 publications

References 23 publications

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA

Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

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