Development of a PSO-ANN Model for Rainfall-Runoff Response in Basins, Case Study: Karaj Basin

Motahari, M; Mazandaranizadeh, Hamed

doi:10.28991/cej-2017-00000070

Cited by 13 publications

(4 citation statements)

References 17 publications

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“…The results showed that the forecasting models are sensitive to different environmental input variables. AI-based models, especially ANN techniques, were frequently applied for forecasting purposes with streamflow and environmental processes thanks to easy implementation, low computational cost and suitable performance (Fotovatikhah et al, 2018;Motahari & Mazandaranizadeh, 2017;Olyaie, Banejad, Chau, & Melesse, 2015;Wang, Xu, Chau, & Lei, 2014;Zamanisabzi, King, Dilekli, Shoghli, & Abudu, 2018). They have been developed to predict a water quality index (Gazzaz, Yusoff, Aris, Juahir, & Ramli, 2012), monthly chemical oxygen demand concentration (Khalil, Awadallah, Karaman, & El-Sayed, 2012), daily water temperature, salinity and dissolved oxygen (Alizadeh & Kavianpour, 2015), etc.…”

Section: Introductionmentioning

confidence: 99%

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Shamshirband

Nodoushan

Adolf

et al. 2018

Engineering Applications of Computational Fluid Mechanics

136

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Section: Introductionmentioning

confidence: 99%

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Shamshirband

Nodoushan

Adolf

et al. 2018

Engineering Applications of Computational Fluid Mechanics

136

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show abstract

“…Applications of different machine learning techniques such as genetic algorithm, artificial neural network and fuzzy inference system into water quality have been reviewed by K.-W. Chau (2006). ANN and ELM models have been employed for water quality forecasting in rivers and seas (Alizadeh & Kavianpour, 2015;Dogan, Sengorur, & Koklu, 2009;Nodoushan, 2018;Tomić, Antanasijević, Ristić, Perić-Grujić, & Pocajt, 2018;Wu, Wang, Chen, Cai, & Deng, 2018), for DO concentration modeling (Heddam & Kisi, 2017), for river discharge monitoring (Garel & D'Alimonte, 2017;Motahari & Mazandaranizadeh, 2017) and for analysis of chlorophyll dynamics (Tian, Liao, & Zhang, 2017). Fotovatikhah et al (2018) provided a comprehensive survey on the computational intelligence applications in flood management systems.…”

Section: Introductionmentioning

confidence: 99%

Effect of river flow on the quality of estuarine and coastal waters using machine learning models

Alizadeh

Kavianpour

Danesh

et al. 2018

Engineering Applications of Computational Fluid Mechanics

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This study explores the river-flow-induced impacts on the performance of machine learning models applied for forecasting of water quality parameters in the coastal waters in Hilo Bay, Pacific Ocean. For this purpose, hourly recorded water quality parameters of salinity, temperature and turbidity as well as the flow data of the Wailuku River were used. Several machine learning models including artificial neural network, extreme learning machine and support vector regression have been employed to investigate the river-flow-induced impact on the water quality parameters from the current time up to 2 h ahead. Following the input structure of the machine learning models, two separate models based on including and excluding the river flow were developed for each variable to quantify the importance of the flow discharge on the accuracy of the forecasting models. The performance of different machine learning models was found to be close to each other and showing similar pattern considering accuracy and uncertainty of the forecasts. The results revealed that flow discharge influenced the water salinity and turbidity of the bay in which the models including the river flow as input variables had better performance compared with those excluding the flow time series. Among the water quality parameters investigated in this research, river flow made the most and least improvement on the efficiency of the models applied for forecasting of turbidity and water temperature, respectively. Overall, it was observed that water quality parameters can be properly forecasted up to several hours ahead providing a potentially valuable tool for environmental management and monitoring in coastal areas.

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“…PSO is a population-based stochastic optimization technique inspired by the social behavior of bird flocking (Motahari & Mazandaranizadeh 2017). In PSO, all particles have a fitness value, which is determined by the fitness function.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A novel medium- and long-term runoff combined forecasting model based on different lag periods

Song

Xiong

et al. 2022

Journal of Hydroinformatics

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The accuracy of medium- and long-term runoff forecasting plays a signiﬁcant role in several applications involving the management of hydrological resources, such as power generation, water supply and ﬂood mitigation. Numerous studies that adopted combined forecasting models to enhance runoff forecasting accuracy have been proposed. Nevertheless, some models do not take into account the effects of different lag periods on the selection of input factors. Based on this, this paper proposed a novel medium- and long-term runoff combined forecasting model based on different lag periods. In this approach, the factors are initially selected by the time-delay correlation analysis method of different lag periods and further screened with stepwise regression analysis. Next, an extreme learning machine (ELM) is adopted to integrate each result obtained from the three single models, including multiple linear regression (MLR), feed-forward back propagation-neural network (FFBP-NN) and support vector regression (SVR), which is optimized by particle swarm optimization (PSO). To verify the effectiveness and versatility of the proposed combined model, the Lianghekou and Jinping hydrological stations from the Yalong River basin, China, are utilized as case studies. The experimental results indicate that compared with MLR, FFBP-NN, SVR and ridge regression (RR), the proposed combined model can better improve the accuracy of medium- and long-term runoff forecasting in the statistical indices of MAE, MAPE, RMSE, DC, U95 and reliability.

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Development of a PSO-ANN Model for Rainfall-Runoff Response in Basins, Case Study: Karaj Basin

Cited by 13 publications

References 17 publications

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters

Effect of river flow on the quality of estuarine and coastal waters using machine learning models

A novel medium- and long-term runoff combined forecasting model based on different lag periods

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