Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data

Farsi, Mohammed; Hosahalli, Doreswamy; Manjunatha, B.R.; Gad, Ibrahim; Atlam, El‐Sayed; Ahmed, Althobaiti; Ghada, Elmarhomy; Elmarhoumy, Mahmoud; Ghoneim, Osama A.

doi:10.1016/j.aej.2020.10.052

Cited by 61 publications

(26 citation statements)

References 27 publications

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“…For the prediction of ocean temperature changes, we use the seasonal ARIMA time series model, which can effectively predict the overall seasonal temperature changes in the target sea area in the next 30 years [22][23][24][25][26][27], and then we can determine the future annual average temperature of the target sea area and compare it with the suitable ocean temperature for herring and mackerel; we can get the target migration position of the future fish school.…”

Section: Research Ideasmentioning

confidence: 99%

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

Chen

et al. 2021

Complexity

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Herring and mackerel are two of the most important pillars of Scottish fisheries. In recent years, global warming has caused a gradual rise in ocean temperatures. In order to survive and reproduce, herring and mackerel populations will migrate. This will have a huge impact on Scotland’s fisheries. Therefore, we need to predict the relocation of fish stocks in advance, make timely adjustments to the fishing range, and minimize the loss of the fishing industry. In this article, we subdivide the research target sea area into 39 regions, establish the optimal SARIMA model for each region based on the collected seawater temperature time series data, and take region 13 and region 15 as examples to fit the ARIMA (3, 3, 1) (1, 2, 1) and ARIMA (2, 3, 1) (0, 2, 1) models with a period of 12. The results show that the SARIMA model fits well in all regions and predicts the temperature changes in the studied sea area from 2021 to 2050. Finally, according to the predicted sea temperature in different periods, the migration position of the fish school is predicted.

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Section: Research Ideasmentioning

confidence: 99%

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

Chen

et al. 2021

Complexity

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“…Moreover, strengths, weaknesses, opportunities, and challenges of online learning during the pandemic needs to be studied. Currently, Machine Learning (ML) approaches are commonly used to help solving real life problems based on statistical data [5] , [9] . In this paper, an ML approach was adopted to examine the mental and psychosomatic impacts of online learning on students in the time of COVID-19 pandemic.…”

Section: Introductionmentioning

confidence: 99%

A new approach in identifying the psychological impact of COVID-19 on university student’s academic performance

Atlam

Ewis

El‐Raouf

et al. 2022

Alexandria Engineering Journal

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“…Seasonal and nonseasonal parts are quite similar, except backshifts are involved in seasonal time [36]. SARIMA in mathematical representation is given below in Equation (3) [38].…”

Section: Statistical Modelmentioning

confidence: 99%

“…AR and MA are mathematically represented in Equations ( 4) and ( 5), respectively where Φ is respective weight of lagged values and ɛ is error at respective lagged values [39]. In Equation (3), the B represents backshifts and ω value for noise at time t [38]. W t is stationary variable, which can further be mathematically explained as Equation ( 6) [40].…”

Section: Statistical Modelmentioning

confidence: 99%

Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

Khan

Shaikh

Siddiqui

et al. 2022

International Journal of Photoenergy

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The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, R square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

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Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data

Cited by 61 publications

References 27 publications

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

A new approach in identifying the psychological impact of COVID-19 on university student’s academic performance

Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

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