Predicting groundwater drawdown in Zakho region, Northern Iraq, using machine learning models optimized by the whale optimization algorithm DOI
Youssef Kassem,

Idrees Majeed Kareem,

Hindreen Mohammed Nazif

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(22)

Published: Nov. 1, 2024

Language: Английский

Determinants of diffuse solar radiation in urban and peatland areas based on weather and air pollutants DOI

Arnida L. Latifah,

Amandha Affa Auliya,

Inna Syafarina

et al.

Journal of Atmospheric and Solar-Terrestrial Physics, Journal Year: 2025, Volume and Issue: 268, P. 106419 - 106419

Published: Jan. 10, 2025

Language: Английский

Citations

0
The CEEMDAN-EWT-CNN-GRU-SVM Model: A Robust Framework for Decomposing Non-Stationary Time Series, Extracting Data features, and Predicting Solar Radiation DOI Creative Commons
Sharareh Pourebrahim, Akram Seifi,

Mohammad Ehteram

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104267 - 104267

Published: Feb. 1, 2025

Language: Английский

Citations

0
Groundwater quality assessment for irrigation in coastal region (Güzelyurt), Northern Cyprus and importance of empirical model for predicting groundwater quality (electric conductivity) DOI Creative Commons
Hüseyin Gökçekuş, Youssef Kassem,

Temel Rızza

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(8)

Published: April 1, 2025

Language: Английский

Citations

0
A novel integration of Hodrick–Prescott filter (Hp-filter) and wavelet transform (WT) with optimize support vector machine (PSO-SVM) in predicting solar radiation DOI Creative Commons

Shuvendu Pal Shuvo,

Shirshendu Pal Shibazee,

Goutam Paul

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 25, 2025

Abstract Previous research has shown that predicting solar radiation is a challenging issue due to highly nonlinear and noisy climate data. Various hybrid approaches have been applied earlier for prediction, which integrates the Wavelet Transform with various Machine Learning models. This research, therefore, intends further improve performance of these existing To address limitations in handling patterns, this study proposes multi-hybrid model accurately incorporates Hodrick–Prescott Filter (HP-Filter), Discrete (DWT), Support Vector (SVM). The collected data from Bangladesh Meteorological Department two different geological locations Bangladesh, namely Dhaka Chittagong, divided into three categories modeling: 70% training, 15% validation, testing, whereas hyper-parameters SVM were optimized using Particle Swarm Optimization algorithm. proposed approach applies before analyzing DWT strengthen model’s ability capture complicated patterns great detail also make more precise reliable. Several metrics, such as Mean Squared Error (MSE), Root Error, Absolute Percentage Coefficient Determination (R 2 ), considered evaluation. results showed it improves upon traditional by 99.76% 99.77% DWT-SVM 39% 57% terms MSE reduction at respectively. R improved 49% 54% over 4.40% 3.16% model. well captures complex trend radiation; thus, shows its potential be other regions efficient prediction radiation.

Language: Английский

Citations

0
Solar radiation prediction: A multi-model machine learning and deep learning approach DOI Creative Commons

C Vanlalchhuanawmi,

Subhasish Deb, Md. Minarul Islam

et al.

AIP Advances, Journal Year: 2025, Volume and Issue: 15(5)

Published: May 1, 2025

The increasing integration of renewable energies into electrical grids necessitates accurate forecasting meteorological variables, particularly solar irradiance. This study presents a novel long-term irradiance approach, utilizing data from the National Renewable Energy Laboratory spanning 1988–2022. Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this evaluates predictive performance 13 data-driven models, comprising ten machine learning (ML) three deep (DL) algorithms. Among them, gradient boosting regressor (GBR) recurrent neural network (RNN) emerged as top performers in ML learning, respectively. In order to choose most suitable model for long short term, four forecast time-horizons (1, 8, 16, 24 h) were also taken consideration models. A feature selection process using Pearson’s coefficient identified relevant inputs, while quantile regression was employed uncertainty assessment, mean prediction interval, interval coverage probability demonstrates that RNN excels short-term predictions, GBR is more effective forecasts. new hybrid approach GBR-RNN developed, achieving superior terms RMSE, MAE, R2 metrics. multi-model integrating both DL techniques, enhances by addressing considering various horizons. findings contribute ongoing advancement energy providing robust, accurate, uncertainty-aware Moreover, this helps identify best-performing model, enabling reliable precise forecasts management. highlights improvement methods importance selecting best accuracy.

Language: Английский

Citations

0
Sunspot number-based neural network model for global solar radiation estimation in Ghardaïa DOI Open Access

Thameur Obeidi,

Bakhti Damani,

Mohamed Khaleel

et al.

STUDIES IN ENGINEERING AND EXACT SCIENCES, Journal Year: 2024, Volume and Issue: 5(2), P. e7156 - e7156

Published: Aug. 27, 2024

In this investigation, the estimation of global solar radiation was meticulously carried out within Ghardaïa city, a region situated in Southern Algeria, utilizing sophisticated multilayer perceptron (MLP) neural network architecture. This research primarily concentrated on developing predictive model based singular input parameter, specifically, sunspot numbers, to forecast levels. The model's formulation rooted empirical data collected over an extensive period from 1984 2000, which used for training network. To assess accuracy and robustness, years 2001 2004 were employed validation purposes. outcomes study highly satisfactory, indicating that MLP-based possesses significant capability Diffuse Global Solar Radiation (DGSR). is substantiated by robust statistical metrics, including normalized Root Mean Square Error (nRMSE) 0.076, reflecting prediction, correlation coefficient (R) 93.16%, denoting strong between predicted observed values. These results underscore efficacy potential application accurately estimating specified region.

Language: Английский

Citations

1
New Categorized Machine Learning Models for Daily Solar Irradiation Estimation in Southern Morocco's, Zagora City DOI Creative Commons
Zineb Bounoua, Laila Ouazzani Chahidi, Abdellah Mechaqrane

et al.

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2024, Volume and Issue: unknown, P. 100777 - 100777

Published: Sept. 1, 2024

Language: Английский

Citations

1
Machine learning for high-performance solar radiation prediction DOI Creative Commons
Irfan Khan, Ali Mehdi, Abeer D. Algarni

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 4794 - 4804

Published: Nov. 4, 2024

Language: Английский

Citations

1
Machine Learning and Wavelet Transform: A Hybrid Approach to Predicting Ammonia Levels in Poultry Farms DOI Creative Commons
Erdem Küçüktopçu, Bilal Cemek, Halis Şimşek

et al.

Animals, Journal Year: 2024, Volume and Issue: 14(20), P. 2951 - 2951

Published: Oct. 14, 2024

Ammonia (NH

Language: Английский

Citations

1
Comparative Analysis of Solar Irradiation Prediction using Machine Learning Models DOI

C Vanlalchhuanawmi,

Subhasish Deb

2022 4th International Conference on Energy, Power and Environment (ICEPE), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 6

Published: June 20, 2024

Language: Английский

Citations

0