Improving Earth surface temperature forecasting through the optimization of deep learning hyper-parameters using Barnacles Mating Optimizer DOI Creative Commons
Zuriani Mustaffa, Mohd Herwan Sulaiman, Muhammad Arif Mohamad

et al.

Franklin Open, Journal Year: 2024, Volume and Issue: 8, P. 100137 - 100137

Published: July 14, 2024

Time series forecasting is crucial across various sectors, aiding stakeholders in making informed decisions, planning for the short and long term, managing risks, optimizing profits, ensuring safety. One significant application of time predicting Earth surface temperatures, which vital civil environmental sectors such as agriculture, energy, meteorology. This study proposes a hybrid model temperature using Deep Learning (DL). To improve DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) integrated to optimize both weights biases. The trained on global dataset with seven inputs compared models optimized by Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA), Ant Colony (ACO). Additionally, comparison made Autoregressive Moving Average (ARIMA) method. Evaluation Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2) demonstrates superior performance BMO, showing minimal errors.

Language: Английский

Enhanced multi-step streamflow series forecasting using hybrid signal decomposition and optimized reservoir computing models DOI
José Henrique Kleinübing Larcher, Stéfano Frizzo Stefenon, Leandro dos Santos Coelho

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124856 - 124856

Published: July 24, 2024

Language: Английский

Citations

9
Assessment of hybrid kernel function in extreme support vector regression model for streamflow time series forecasting based on a bayesian estimator decomposition algorithm DOI
Peng Shi, Lei Xu, Simin Qu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 149, P. 110514 - 110514

Published: March 15, 2025

Language: Английский

Citations

1
Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review DOI Creative Commons
Crescenzo Pepe, Silvia Maria Zanoli

Energies, Journal Year: 2024, Volume and Issue: 17(4), P. 941 - 941

Published: Feb. 17, 2024

Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein exploited as a renewable resource source energy production. usually include reservoirs, valves, gates, production devices, e.g., turbines. In this context, monitoring maintenance policies together with control optimization strategies, at different levels of automation hierarchy, may strategic tools drivers efficiency improvement. Nowadays, these strategies rely on basic concepts elements, which must be assessed investigated provide reliable background. This paper focuses review state art associated i.e., digitalization, Industry 4.0, data, KPIs, modelization, forecast.

Language: Английский

Citations

4
Informer–SVR: Traffic Volume Prediction Hybrid Model Considering Residual Autoregression Correction DOI
Chang Xu, Yichen Chen, Qingwei Zeng

et al.

Journal of Transportation Engineering Part A Systems, Journal Year: 2025, Volume and Issue: 151(4)

Published: Jan. 23, 2025

Language: Английский

Citations

0
A Novel Stochastic Tree Model for Daily Streamflow Prediction Based on A Noise Suppression Hybridization Algorithm and Efficient Uncertainty Quantification DOI
Nasrin Fathollahzadeh Attar, Mohammad Taghi Sattari, Halit Apaydın

et al.

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(6), P. 1943 - 1964

Published: Feb. 7, 2024

Language: Английский

Citations

2
Temporal characteristics-based adversarial attacks on time series forecasting DOI
Ziyu Shen, Yun Li

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125950 - 125950

Published: Nov. 1, 2024

Language: Английский

Citations

1
Improving Earth surface temperature forecasting through the optimization of deep learning hyper-parameters using Barnacles Mating Optimizer DOI Creative Commons
Zuriani Mustaffa, Mohd Herwan Sulaiman, Muhammad Arif Mohamad

et al.

Franklin Open, Journal Year: 2024, Volume and Issue: 8, P. 100137 - 100137

Published: July 14, 2024

Time series forecasting is crucial across various sectors, aiding stakeholders in making informed decisions, planning for the short and long term, managing risks, optimizing profits, ensuring safety. One significant application of time predicting Earth surface temperatures, which vital civil environmental sectors such as agriculture, energy, meteorology. This study proposes a hybrid model temperature using Deep Learning (DL). To improve DL model's performance, an optimization algorithm called Barnacles Mating Optimizer (BMO) integrated to optimize both weights biases. The trained on global dataset with seven inputs compared models optimized by Particle Swarm Optimization (PSO), Harmony Search Algorithm (HSA), Ant Colony (ACO). Additionally, comparison made Autoregressive Moving Average (ARIMA) method. Evaluation Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2) demonstrates superior performance BMO, showing minimal errors.

Language: Английский

Citations

0