Modelling daily water level fluctuations of Lake Van (Eastern Turkey) using Artificial Neural Networks

Doğan, Emrah; Kocamaz, Uğur Erkin; Utkucu, Murat; Yildirim, Eray

doi:10.1127/fal/2015/0736

Cited by 9 publications

(7 citation statements)

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“…Empirical evidence that is not entirely conclusive, since in direct comparison with the work of Dogan et al [31] and Lake Van, we see that the model based on neural networks of this latter is superior, since it obtains a lower MSE, in this case of 0.0012. The set of training data includes only 560 observations, for the 8036 observations used by Dogan et al [31]. In this regard, we conclude that the best way to proceed is from daily data on water levels in the reservoir.…”

Section: Exploration Of Forecasting Techniquescontrasting

confidence: 61%

“…We emphasize this research by Dogan et al [31], especially through the procedure, which has proved to be especially interesting and unmarked with the rest we have seen. It captures data from October 1975 to December 2011.…”

Section: Exploration Of Forecasting Techniquesmentioning

confidence: 66%

“…It would have been interesting to see the integration of the three variables in a single model to predict the only real objective variable, such as the level of water in the reservoir. Dogan et al [31] compile a comparison of neural networks similar to the research seen until now, on the Lake Van in eastern Turkey. The work done by Sen et al [28] take into account the data of volume of water in the reservoir when developing the predictive model, despite recognising that this volume depends on different factors such as complex meteorological and geological.…”

Section: Exploration Of Forecasting Techniquesmentioning

confidence: 98%

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Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

2019

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Reservoirs are natural or artificial lakes used as a source of water supply for society daily applications. In addition, hydroelectric power plants produce electricity while water flows through the reservoir. However, reservoirs are limited natural resources since water levels vary according to annual rainfalls and other natural events, and consequently, the energy generation. Therefore, forecasting techniques are helpful to predict water level, and thus, electricity production. This paper examines state-of-the-art methods to predict the water level in Catalan reservoirs comparing two approaches: using the water level uniquely, uni-variant; and adding meteorological data, multi-variant. With respect to relating works, our contribution includes a longer times series prediction keeping a high precision. The results return that combining Support Vector Machine and the multi-variant approach provides the highest precision with an R 2 value of 0.99.

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Section: Exploration Of Forecasting Techniquescontrasting

confidence: 61%

Section: Exploration Of Forecasting Techniquesmentioning

confidence: 66%

Section: Exploration Of Forecasting Techniquesmentioning

confidence: 98%

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Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

2019

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“…The results of the analysis were compared using the mean square error (MSE) and R 2 coefficient of determination, and it was stated that the FFNN algorithm model performed better than the RBFNN algorithm model. According to the estimation results obtained with this study, it was concluded that there will be a decrease in the water level of Lake Van in the future, therefore, the increase of water in the rapidly developing and dense settlements around the coast of Lake Van will not become a threat (Doğan et al, 2016). In another study, using daily flow values in the state of Pennsylvania, the Juniata River, which has an 8687 km 2 drainage area without any dam in its basin, was selected by black-box modelling.…”

Section: Introductionmentioning

confidence: 71%

Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity

Temiz

DAMLA²,

KESKİN

2022

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Making estimation of river flow with hydrological and methodological data of the past period and using in water resources project studies have been used for a long time in many studies in our country and in the world. In addition to this, along with frequent drought problems in recent years, it is also important to store and use the water in the reservoirs correctly. This study aims to research how two separate Artificial-Neural-Network-Functions, which are generated based on the study of neural networks in the human brain, can be used in areas with a certain reservoir capacity such as dams or ponds and to provide an example of the most appropriate ANN model for predicting the level changes that will occur depending on the next years. In this context, two separate functions have been evaluated. These are the Gradient-Descent-with-Momentum (GDM) and Levenberg-Marquardt-(LM) training functions. Here, instead of the best results obtained from the models, the best three model outputs were evaluated and the models were considered from a wide frame. Gökçe Dam Basin, which is located in the Marmara region was chosen as the study area. With the different architectures prepared for the period of 31 December 2019 -1 January 2000, monthly basin capacities for 2019 have been tried to be estimated. 95% of the data belonging to the selected period was used for training and the remaining 5% for testing purposes. The flow rates entering and leaving the dam basin and the average precipitation, evaporation, and dam seepage amount were used as model inputs. While evaluating the model performances, Mean squared error (MSE), Mean absolute percent error (MAPE), Mean absolute error (MAE), and coefficients of determination were taken into consideration. As a result; It can be stated that the LM training models are more successful in estimating the dam basin levels and converge to the real values.

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“…Lake Van is the largest body of water in Turkey with a total area of 3,750 km 2 and the depth of the lake reaches up to 450 meters (Doğan et al, 2016). The eastern part of the lake is in the territory of Van province, while the western part of the lake is in the territory of Bitlis province.…”

Section: Study Areamentioning

confidence: 99%

Exceedance probability assessment of bathing water quality standards in lake Van based on a geostatistical analysis

Sanli

Yılmazel

2021

International Journal of Agriculture Environment and Food Sciences

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Monitoring bathing water quality (BWQ) is highly important in Turkey both for public health issues and tourism income. Lake Van is one of the largest lakes of Turkey and serves as one of the most important tourist attractions in the eastern part of Turkey. This study aims to assess critical bathing sites in Lake Van by using historical BWQ data that was collected twice a month during the swimming season from June 15 to August 31, between the years 2010 and 2020. To avoid public health hazards, it is very important to determine the spatial dimension of inland water pollution and provide visual tools for its presentation. Geostatistical data analysis and the determination of critical locations have been done by a spatial interpolation method, named Probability Kriging (PK) using Geographic Information System (GIS) based software ArcMap 10. Probability maps for exceeding the threshold values identified for the two microbiological water quality parameters of Escherichia coli and intestinal enterococci (IE) were generated, and used for the identification of four critical regions: İskele (4), west side of Gevaş (1), Edremit (3) and Muradiye/Erciş (7). Insufficient wastewater treatment plant capacity serving to high population in these regions may indicate the most pressing issues disturbing the BWQ.

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Modelling daily water level fluctuations of Lake Van (Eastern Turkey) using Artificial Neural Networks

Cited by 9 publications

References 0 publications

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

Predicting Energy Generation Using Forecasting Techniques in Catalan Reservoirs

Comparison of Different Artificial Neural Network Methods in Determining Reservoir Capacity

Exceedance probability assessment of bathing water quality standards in lake Van based on a geostatistical analysis

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