Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Kilinç, Hüseyin Çağan

doi:10.3390/w14030490

Cited by 32 publications

(10 citation statements)

References 46 publications

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“…Khan et al, 2016), which makes it appealing for use in a variety of domains, such as water demand prediction (Ömer Faruk, 2010;Zubaidi, Abdulkareem et al, 2020;Zubaidi, Dooley et al, 2018;, streamflow forecasting (Apaydin et al, 2021;F.F. Li et al, 2021;Gunathilake et al, 2021;Huang et al, 2021;Kilinc, 2022;Kilinc & Haznedar, 2022;Kim et al, 2022;Niu & Feng, 2021;Rahimzad et al, 2021), water quality predictions (Abba et al, 2017;W. Li et al, 2020;Sami et al, 2021;Stamenković, 2021;Tahraoui et al, 2021;Y.-F. Zhang et al, 2020), and drought prediction (Adamowski et al, 2012;Ahmadi et al, 2021;Dikshit et al, 2020;M.M.H.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Machine learning (ML) techniques can learn from past experience (data) and create new (mathematical) models that can be applied to new data. ML has been applied in many areas of the hydrology fields, for example, stream flow (Kilinc, 2022;Kilinc & Haznedar, 2022). There are different types of ML used in the field of water level prediction, such as artificial neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), support vector regression (SVR), and a variety of hybrid models (Çimen & Kisi, 2009;B.…”

Section: Introductionmentioning

confidence: 99%

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Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

et al. 2022

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The community's well-being and economic livelihoods are heavily influenced by the water level of watersheds. The changes in water levels directly affect the circulation processes of lakes and rivers that control water mixing and bottom sediment resuspension, further affecting water quality and aquatic ecosystems. Thus, these considerations have made the water level monitoring process essential to save the environment. Machine learning hybrid models are emerging robust tools that are successfully applied for water level monitoring. Various models have been developed, and selecting the optimal model would be a lengthy procedure. A timely, detailed, and instructive overview of the models' concepts and historical uses would be beneficial in preventing researchers from overlooking models' potential selection and saving significant time on the problem. Thus, recent research on water level prediction using hybrid machines is reviewed in this article to present the "state of the art" on the subject and provide some suggestions on research methodologies and models. This comprehensive study classifies hybrid models into four types algorithm parameter optimisation-based hybrid models (OBH), pre-processingbased hybrid models (PBH), the components combination-based hybrid models (CBH), and hybridisation of parameter optimisation-based with preprocessing-based hybrid models (HOPH); furthermore, it explains the pre-processing of data in detail. Finally, the most popular optimisation methods and future perspectives and conclusions have been discussed.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

et al. 2022

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“…The comparison results showed that all models have excellent prediction effects. Moreover, in this study, two important parameters, the number of hidden-layer nodes and the learning rate of the LSTM model, were optimized by the PSO algorithm [57], and the optimized BiLSTM model was compared with the benchmark LSTM model. The results showed that the PSO algorithm-based model parameter optimization and the BiLSTM structure can improve the prediction performance of the benchmark LSTM model.…”

Section: Prospect Of Hydrologic Application Of Pso-lstm and Bilstm Mo...mentioning

confidence: 99%

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Yang

Maihemuti

Simayi

et al. 2022

Water

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The simulation and prediction of glacially derived runoff are significant for water resource management and sustainable development in water-stressed arid regions. However, the application of a hydrological model in such regions is typically limited by the intricate runoff production mechanism, which is associated with snow and ice melting, and sparse monitoring data over glacierized headwaters. To address these limitations, this study develops a set of mathematical models with a certain physical significance and an efficient particle swarm optimization algorithm by applying long- and short-term memory networks on the glacierized Muzati River basin. First, the trends in the runoff, precipitation, and air temperature are analyzed from 1990 to 2015, and differences in their correlations in this period are exposed. Then, Particle Swarm Optimization–Long Short-Term Memory (PSO-LSTM) and Bi-directional Long Short-Term Memory (BiLSTM) models are combined and applied to the precipitation and air temperature data to predict the glacially derived runoff. The prediction accuracy is validated by the observed runoff at the river outlet at the Pochengzi hydrological station. Finally, two other types of models, the RF (Random Forest) and LSTM (Long Short-Term Memory) models, are constructed to verify the prediction results. The results indicate that the glacially derived runoff is strongly correlated with air temperature and precipitation. However, in the study region over the past 26 years, the air temperature was not obviously increasing, and the precipitation and glacially derived runoff were significantly decreasing. The test results show that the PSO-LSTM and BiLSTM runoff prediction models perform better than the RF and LSTM models in the glacierized Muzati River basin. In the validation period, among all models, the PSO-LSTM model has the smallest mean absolute error and root-mean-square error and the largest coefficient of determination of 6.082, 8.034, and 0.973, respectively. It is followed by the BiLSTM model having a mean absolute error, root-mean-square error, and coefficient of determination of 6.751, 9.083, and 0.972, respectively. These results imply that both the particle swarm optimization algorithm and the bi-directional structure can effectively enhance the prediction accuracy of the baseline LSTM model. The results presented in this study can provide a deeper understanding and a more appropriate method of predicting the glacially derived runoff in glacier-fed river basins.

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“…The traditional LSTM model will show obvious instability in the training process, and even the gradient will disappear. [48][49][50] Therefore, this study proposes PSO-CS optimization algorithm to iteratively optimize the LSTM model and find the optimal parameters of the model. That is, the PSO-CS algorithm is used to optimize the two key parameters of LSTM model (the number of neurons m and the learning rate lr).…”

Section: Long Short-term Memory Modelmentioning

confidence: 99%

Intelligent research on wearing comfort of tight sportswear during exercise

Cheng

Wang

Zeng

et al. 2022

Journal of Industrial Textiles

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In this study, the distribution characteristics and changing law of sports comfort perception were analyzed by collecting the comfort evaluation data of running in winter tight sportswear, and proposes a network model based on particle swarm optimization-cuckoo search-long short-term memory to track the changing law of motion comfort. First, considering the existence of redundant features, analytic hierarchy process analysis is used to screen out key features; and then, particle swarm optimization and cuckoo search algorithms are used to optimize the key parameters of the long short-term memory prediction model, so as to avoid the model prediction performance caused by the selection of parameters based on experience. The experiments compared the prediction accuracy of other models, and selected mean absolute error, root mean square error, and mean absolute percentage error evaluation indicators to verify the effectiveness of these models. The results show that the perception of wearing comfort changes over time, but when it reaches the extreme point at a certain moment, and then it gradually falls back. The humidity sense and thermal sense of bust, crotch, and back in human body are the main comfort perceptions that affect movement; LSTM and the optimized LSTM models are suitable for the prediction of comfort perception at different times during exercise. Among them, the PSO-CS-LSTM model can more accurately track the changing trend of motion comfort, the prediction has high prediction accuracy and validity; we selected three different running speeds as the experimental data, which also verifies the universal applicability of the model.

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Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin

Cited by 32 publications

References 46 publications

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

Application of hybrid machine learning models and data pre-processing to predict water level of watersheds: Recent trends and future perspective

Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model

Intelligent research on wearing comfort of tight sportswear during exercise

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