Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey

Kurunç, Ahmet; Yürekli, Kadri; Çevik, Osman

doi:10.1016/j.envsoft.2004.11.001

Cited by 91 publications

(49 citation statements)

References 7 publications

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“…In addition, rising investments through mega-projects such as construction of The Yavuz Sultan Selim Bridge, The Third Airport with 150 million passenger capacities and The New City Project have a strong potential for increasing the city population as well as demand for clean water in the near future. Hence, much more attention should be given to probabilistic forecasting methods in order to reflect the role of uncertainty in future supply and demand forecasts of water in Istanbul [6,36]. In addition, real time water demand and supply tracking and management of city water through information technology (IT) are likely to be inevitable in the future and the importance of the algorithm-based IT applications and knowledge-based urban development is highlighted in the previous studies [37,38].…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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Abstract:The metropolitan city of Istanbul is becoming overcrowded and the demand for clean water is steeply rising in the city. The use of analytical approaches has become more and more critical for forecasting the water supply and demand balance in the long run. In this research, Istanbul's water supply and demand data is collected for the period during 2006 and 2014. Then, using an autoregressive integrated moving average (ARIMA) model, the time series water supply and demand forecasting model is constructed for the period between 2015 and 2018. Three important sustainability metrics such as water loss to supply ratio, water loss to demand ratio, and water loss to residential demand ratio are also presented. The findings show that residential water demand is responsible for nearly 80% of total water use and the consumption categories including commercial, industrial, agriculture, outdoor, and others have a lower share in total water demand. The results also show that there is a considerable water loss in the water distribution system which requires significant investments on the water supply networks. Furthermore, the forecasting results indicated that pipeline projects will be critical in the near future due to expected increases in the total water demand of Istanbul. The authors suggest that sustainable management of water can be achieved by reducing the residential water use through the use of water efficient OPEN ACCESSSustainability 2015, 7 11051 technologies in households and reduction in water supply loss through investments on distribution infrastructure.

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

et al. 2015

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“…However, since no individual parameter can express the WQ sufficiently, the WQ is normally assessed by measuring a broad range of parameters (temperature; pH; electric conductivity (EC); total dissolved solids (TDS); and the concentrations of the heavy metals).Although, parametric statistical and deterministic models have been traditional way for modeling the water quality, but these require vast information on various hydro logical sub-processes in order to arrive the end results. In recent years, several researches have been conducted on water quality forecast models (10,11). However, since a large number of factors affecting the water quality have a complicated non-linear relation with the variables; traditional data processing methods are no longer good enough for solving the problem (12,13).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

2013

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Background: Access to safe drinking water is one of the basic human rights and essential for healthy life. Concerns about the effects of copper on human health have led to numerous guidelines and regulations limiting its concentrations in water. Objectives: The major goal of this study is to demonstrate artificial neural network model of the Chahnimeh1 reservoir water quality (Heavy metal concentration) and show the potential of the ANN for producing models capable of efficient forecasting of Cu concentration. Materials and Methods: Water samples were collected from Chahnimeh1 reservoir which was the most important source of drinking water in Sistan-balochistan and analyzed for physical quality parameters such as: EC (electric conductivity), TDS(total dissolved solids), T(temperature), pH and heavy metal (Cu) concentration using standard methods. In this study, a three-layer artificial neural network (ANN) model was investigated to predict the Cu concentration in the water of Chahnimeh1 reservoir. The input variables are electric conductivity, total dissolved solids, temperature and pH, while the Cu concentration in water is the output. We applied The Levenberg-Marquardt (LM) algorithm to train ANN. Results: According to the ANN outputs, hidden layer with 7 neurons had the best performance for predicting Cu concentration. Evaluation indexes including MSE and R in this article were obtained as 0.00008 and 0.9346; 0.00019 and 0.8612; 0.00014 and 0.9372 for training, validation and testing date sets respectively. Conclusions: As we can see the ANN outputs values are very close to actual Cu concentration, so indicating that predicted values are accurate and the network design is proper and the input variables well suitable for the prediction of Cu concentration.

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“…(Mondal and Wasimi 2007) Several applications of all of these models for analysis of fluctuations of groundwater level over time are vastly used in several groundwater hydrology applications (Mondal and Wasimi 2006). Furthermore, they have extensive use in various areas of science and engineering such as forecasting of river discharge and synthetic data generation (Salas and Obeysekera 1982;Mondal and Wasimi 2003) stream flow and water quality data prediction (Mondal and Wasimi 2005;Kurunc et al 2005). Water resource monitoring and assessment (Mondal and Wasimi 2003) and drought period simulation (Durdu 2010).…”

Section: Introductionmentioning

confidence: 99%

Simulation and analysis of temporal changes of groundwater depth using time series modeling

Khorasani

Ehteshami

Ghadimi

et al. 2016

Model. Earth Syst. Environ.

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In the world's water scarce regions, groundwater as an important and strategic resource needs proper assessment. An accurate forecasting needs to be performed in order to make a better identification of fluctuating nature of groundwater levels. In this study, groundwater level fluctuations of Kabudarahang aquifer was synchronized and verified. Investigation was conducted with usage and application of time series models. Groundwater level data during 2003-2014 are used for calibration and analyses were performed using Box-Jenkins models. Residual error analysis and comparison of observed and calculated groundwater levels were performed. Then a prediction model for groundwater level in Kabudarahang aquifer developed. The model was used for predicting the groundwater level during 2014-2017. Model results showed that the groundwater level in this aquifer will endure a 5 m decline for the next three upcoming years.

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Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey

Cited by 91 publications

References 7 publications

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

A Framework for Sustainable Urban Water Management through Demand and Supply Forecasting: The Case of Istanbul

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

Simulation and analysis of temporal changes of groundwater depth using time series modeling

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