Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher; Faris, Hossam; Al‐Ansari, Nadhir

doi:10.1155/2020/8206245

Cited by 53 publications

(26 citation statements)

References 53 publications

(63 reference statements)

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“…Furthermore, ELM is consistent with commonly all non-linear activation functions. (2) Figure. 2 Extreme learning machine structure [27] Or can be expressed in matrix form as =…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

Hasan¹,

Mahmoud²,

Hasan³

2021

IJIES

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Recently, a new challenge to the researchers has been emerged due to the spread of a new outbreak called novel corona-virus (COVID-19) to portend the confirmed cases. Since discovering the first certain cases in Wuhan, China, the COVID-19 has been widely spreading and expanding to other provinces and other countries via travellers into various countries around the world. COVID-19 virus is not a problem of only developing countries, but also of developed countries. Artificial Intelligence (AI) techniques can be profitable to predict such; parameters, risks and influence of an outbreak. Therefore, accurate prognosis can be helpful to control the spread of such viruses and provide crucial information for identifying the type of virus interventions and intensity. Here are develop an intelligent model depicting COVID-19 transmission and resulting confirmed cases. The epidemic curve of COVID-19 cases was modelled. The main key idea of predicting the confirmed cases is based on two factors 1) cumulative number of confirmed cases and 2) the daily confirmed cases instead of using only one factor as previous research. In this study, a comparison between different intelligent techniques has been conducted. To assess the effectiveness of these intelligent models, a recorded data of 5 months has been used for training in various countries. Results obtained show the superiority of ELM model in accurate prediction and outperforms other intelligent techniques. We have used a Social Spider Optimization (SSO) method to optimize the ELM parameters. The prediction results show the superiority of the proposed intelligent predictors with accuracy greater than 93%. Therefore, medical personnel can take defensive steps earlier.

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“…Furthermore, ELM is consistent with commonly all non-linear activation functions. (2) Figure. 2 Extreme learning machine structure [27] Or can be expressed in matrix form as =…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

Hasan¹,

Mahmoud²,

Hasan³

2021

IJIES

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“…e climate of the basin is predominantly arid; however, semiaridity is the main characteristic of the river. e average rainfall in the basin is 216 mm with most of the rainfall occurring during winter (December to February) [28]. However, the rainfall concentration is varied from the north, middle, and south of Iraq [29].…”

Section: Description Of Study Areas and Datamentioning

confidence: 99%

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

et al. 2020

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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%–10% data division. In addition, it was found to improve the accuracy in forecasting high flow events. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models.

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“…iv. Future research on groundwater monitoring and detection can be investigated using the feasibility of soft computing models such as artificial intelligence models (Afan et al, 2020;Yaseen et al, 2019 andYaseen et al, 2020).…”

Section: Recommendationsmentioning

confidence: 99%

Surface Water-Groundwater Interaction in Diwaniya, Southern Iraq Using Isotopic and Chemical Techniques

Hadi¹

2020

IGJ

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The stable isotopes of 2 H, 18 O, and 3 H reinforced by hydrochemical data, were used to identify the interaction between groundwater and Shatt Al-Diwaniya water and estimate the age of groundwater in the research area. For achieving these two goals, twenty-two samples of water were collected at the suggested area through a sampling campaign for twelve shallow wells, three from Shatt Al-Diwaniya, six rainfall samples, and one sample of sewer water. Three additional samples were collected for comparison from outside of the study area. The results of stable isotopes and hydrochemical composition indicated that there is a similarity between shallow groundwater samples and Shatt Al-Diwaniya water. The surface water interacts with groundwater for all boreholes, except for the borehole in the Al-Jameih District (WR2), which showed similar results spring water as comparing with SW1 and SW2 spring water samples. The isotopic composition of Shatt Al-Diwaniya (2 H and 18 O) is close to the local, and meteoric global water line, this indicates that the rainfall makes a significant contribution to the water of Shatt-Aldiwaniya. There is a convergence with the values of isotopes of wastewater and groundwater, which indicates that the formation of shallow groundwater is a mixture of Shatt Al-Diwaniya water and the recharge (wastewater and rainfall). The results of 3 H concentration remaining in shallow groundwater showed that the age of the groundwater ranges from1.8 to 6.3 years, this indicates that recharge of shallow groundwater is modern.

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Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application

Cited by 53 publications

References 53 publications

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

Optimized Extreme Learning Machine for Forecasting Confirmed Cases of COVID-19

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

Surface Water-Groundwater Interaction in Diwaniya, Southern Iraq Using Isotopic and Chemical Techniques

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