Utilization of PSO algorithm in estimation of water level change of Lake Beysehir

Büyükyıldız, Meral; Tezel, Gülay

doi:10.1007/s00704-015-1660-2

Cited by 9 publications

(2 citation statements)

References 30 publications

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“…Since 1914, Beyşehir Lake has been used for drinking water, irrigation, fishing, commercial activities, and tourism (Buyukyildiz and Tezel, 2017). In addition to serving as a reservoir for irrigation in the Cumra Plain, the lake is also used for navigation purposes (Nas et al, 2009).…”

Section: Study Areamentioning

confidence: 99%

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

Tan Kesgin,

Demir,

Kesgin

et al. 2023

Journal of Water and Land Development

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Beyşehir Lake is the largest freshwater lake in the Mediterranean region of Turkey that is used for drinking and irrigation purposes. The aim of this paper is to examine the potential for data-driven methods to predict long-term lake levels. The surface water level variability was forecast using conventional machine learning models, including autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA). Based on the monthly water levels of Beyşehir Lake from 1992 to 2016, future water levels were predicted up to 24 months in advance. Water level predictions were obtained using conventional time series stochastic models, including autoregressive moving average, autoregressive integrated moving average, and seasonal autoregressive integrated moving average. Using historical records from the same period, prediction models for precipitation and evaporation were also developed. In order to assess the model's accuracy, statistical performance metrics were applied. The results indicated that the seasonal autoregressive integrated moving average model outperformed all other models for lake level, precipitation, and evaporation prediction. The obtained results suggested the importance of incorporating the seasonality component for climate predictions in the region. The findings of this study demonstrated that simple stochastic models are effective in predicting the temporal evolution of hydrometeorological variables and fluctuations in lake water levels.

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Section: Study Areamentioning

confidence: 99%

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

Tan Kesgin,

Demir,

Kesgin

et al. 2023

Journal of Water and Land Development

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“…In general, an ENN, which is trained by the backpropagation algorithm, is prone to fall into local minima, etc. Meanwhile, a particle swarm optimisation (PSO) algorithm can search globally and achieve fast convergence (Buyukyildiz and Tezel, 2015). Therefore, PSO was selected to optimise the ENN as a solution of its defect.…”

Section: Integration Of Elman Neural Network With Particle Swarm Opti...mentioning

confidence: 99%

Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake

et al. 2022

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A reliable water level prediction in a lake system is crucial for water resources management, flood control, etc. The objective of this study is to propose a machine learning model which is able to achieve a considerably high level of accuracy in terms of water level prediction. Dongting Lake, which is the second-largest freshwater lake system in China, was selected as the study area. The hourly water level, flow rate, rainfall and temperature of the upstream water stations and rainfall of the downstream water stations were used as the input features, to predict the water level at the downstream stations. Multilayer perceptron neural network (MLP-NN), Elman neural network (ENN), and integration of particle swarm optimisation algorithm to Elman neural network (PSO-ENN) were selected as the model development techniques. The PSO-ENN model appears as the best performed model, as it records NSE of 0.929–0.988, RMSE of 0.129–0.322 and MAE of 0.151–0.359 at the downstream stations in Dongting Lake. The PSO-ENN model also shows its ability to provide better performance for the water level prediction of 36 h in advance. In terms of input variables sensitivity, the developed model is most sensitive to flow rate, followed by rainfall.

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A Review of Real-World Applications of Particle Swarm Optimization Algorithm

Pluháček

Šenkeřík

Viktorin

et al. 2017

Lecture Notes in Electrical Engineering

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Utilization of PSO algorithm in estimation of water level change of Lake Beysehir

Cited by 9 publications

References 30 publications

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

A data-driven approach to predict hydrometeorological variability and fluctuations in lake water levels

Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake

A Review of Real-World Applications of Particle Swarm Optimization Algorithm

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