Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

Tikhamarine, Yazid; Souag-Gamane, Doudja; Ahmed, Ali Najah; Kisi, Özgür; El-Shafie, Ahmed

doi:10.1016/j.jhydrol.2019.124435

Cited by 200 publications

(57 citation statements)

References 63 publications

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“…In recent years, the use of artificial intelligence by many researchers to solve complex and uncertain problems has become widespread [62][63][64][65][66][67][68][69][70][71], and there have been especially successful applications in the health problems [72][73][74][75][76]. One of these advanced techniques is the particle swarm optimization (PSO) algorithm, first introduced by Kennedy and Eberhart [77][78][79].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Haghshenas

Pirouz

et al. 2020

IJERPH

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Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

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Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Haghshenas

Pirouz

et al. 2020

IJERPH

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“…where, m = number of neurons n = number of inputs [21]. Figure 2 shows the architecture process of the ANN regression approach.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

Latif¹,

Azmi²,

Ahmed³

et al. 2020

IJDNE

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Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

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“…Combining methods can build hybrid models with optimization algorithms, which can be used to identify the optimal parameter combination and calibrate models to enhance their robustness. Many algorithms are applied to improve single model forecasting abilities in hydrology, including the genetic algorithm (GA) [18], Fruit fly Optimization Algorithm (FOA) [19], grey wolf optimizer (GWO) [20], and particle swarm optimization (PSO) [21]. Based on the social hierarchy and hunting behaviors of grey wolves, Mirjalili et al [22] proposed the GWO algorithm, one of the newest swarm intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the social hierarchy and hunting behaviors of grey wolves, Mirjalili et al [22] proposed the GWO algorithm, one of the newest swarm intelligence algorithms. The GWO algorithm avoids several local solutions and balances between exploration and exploitation efficiency [20]. Moreover, it is simple, flexible, and has very few parameters [23].…”

Section: Introductionmentioning

confidence: 99%

Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models

Zhao

Wei

et al. 2021

Water

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Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.

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Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

Cited by 200 publications

References 63 publications

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models

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