Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region DOI Creative Commons
Emad Elabd,

Hany Mohamed Hamouda,

Mazen Ali

et al.

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

Published: May 10, 2025

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems cities. It has worldwide economic consequences. change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With focus on Al-Qassim Region, Saudi Arabia, assesses temperature, air dew point, visibility distance, atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) reduce dataset imbalance. The CNN-GRU-LSTM was compared 5 classic regression models: DTR, RFR, ETR, BRR, K-Nearest Neighbors. Five main measures were evaluate performance: MSE, MAE, MedAE, RMSE, R². After Min-Max normalization, split into training (70%), validation (15%), testing (15%) sets. paper shows beats standard methods in all four climatic scenarios, R² values 99.62%, 99.15%, 99.71%, 99.60%. Deep predicts climate well can guide environmental policy urban development decisions.

Language: Английский

Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms DOI

J. Srinivas,

C. Raju,

C. Sasikala

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

0
Bibliometric Insights into Terracing Effects on Water Resources Under Climate Change: Advances in Remote Sensing and GIS Applications DOI Open Access
Xiaona Du, Guang Yang, Haihong Yuan

et al.

Water, Journal Year: 2025, Volume and Issue: 17(8), P. 1125 - 1125

Published: April 10, 2025

With the increasing impacts of global climate change and continuous expansion population, scarcity food water resources, along with protection agricultural land, have become significant constraints to sustainable development. Terraces plays a vital role in controlling loss promoting agriculture, they been widely adopted across globe. Using CiteSpace, this study conducted bibliometric review literature on application remote sensing GISs terrace studies under change. The dataset included publications from Web Science spanning years 1992 2024. Based systematical analysis 508 publications, we investigated major institutions, cross-author collaborations, keyword co-occurrences, evolution research focus areas regarding applications studies. results show that prominent themes domain include sensing, erosion, China (132, 26%) United States (108, 21%) are top contributors terms publication numbers, while European countries institutions more active collaborative efforts. emphasis has transitioned analyzing environmental characteristics terraces broader consideration ecological factors multi-scenario applications. Moreover, analyses co-occurrence temporal trends indicate rising interest machine learning, deep luminescence dating Moving forward, it is essential advance deployment automated monitoring systems, obtain long-term data, encourage adoption conservation agriculture technology, strengthen early warning networks for extreme events research. Overall, underscores importance interdisciplinary approaches efforts address myriad challenges faced by terraced an era rapid

Language: Английский

Citations

0
Advancing Climate Change Research: Robust Methodology for Precise Mapping of Sea Level Rise Using Satellite-Derived Bathymetry and the Google Earth Engine API. DOI
Mohammad Ashphaq, Pankaj Kumar Srivastava, Debashis Mitra

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: 38, P. 101557 - 101557

Published: April 1, 2025

Language: Английский

Citations

0
Towards Digitalization for Air Pollution Detection: Forecasting Information System of the Environmental Monitoring DOI Open Access
Kyrylo Vadurin, Андрій Перекрест, Volodymyr Bakharev

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 3760 - 3760

Published: April 22, 2025

This study addresses the urgent need for advanced digitalization tools in air pollution detection, particularly within resource-constrained municipal settings like those Ukraine, aligning with directives such as AAQD. The forecasting information system integrating data processing, analysis, and visualization to improve environmental monitoring practices is described this article. utilizes machine learning models (ARIMA BATS) time series forecasting, automatically selecting optimal model based on accuracy metrics. Spatial analysis employing inverse distance weighting (IDW) provides insights into pollutant distribution, while correlation identifies relationships between pollutants. was tested using retrospective from Kremenchuk agglomeration (2007–2024), demonstrating its ability forecast quality parameters identify areas exceeding maximum permissible concentrations. Results indicate that BATS often outperforms ARIMA several key pollutants, highlighting importance of automated selection. developed offers a cost-effective solution local municipalities, enabling data-driven decision-making, optimized network placement, improved alignment European Union standards.

Language: Английский

Citations

0
Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics DOI

Reddi Khasim Shaik,

D. Santhi Jeslet,

Vijay Vasanth Aroulanandam

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Language: Английский

Citations

0
Climate change prediction in Saudi Arabia using a CNN GRU LSTM hybrid deep learning model in al Qassim region DOI Creative Commons
Emad Elabd,

Hany Mohamed Hamouda,

Mazen Ali

et al.

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

Published: May 10, 2025

Climate change, which causes long-term temperature and weather changes, threatens natural ecosystems cities. It has worldwide economic consequences. change trends up to 2050 are predicted using the hybrid model that consists of Convolutional Neural Network-Gated Recurrent Unit-Long Short-Term Memory (CNN-GRU-LSTM), a unique deep learning architecture. With focus on Al-Qassim Region, Saudi Arabia, assesses temperature, air dew point, visibility distance, atmospheric sea-level pressure. We used Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) reduce dataset imbalance. The CNN-GRU-LSTM was compared 5 classic regression models: DTR, RFR, ETR, BRR, K-Nearest Neighbors. Five main measures were evaluate performance: MSE, MAE, MedAE, RMSE, R². After Min-Max normalization, split into training (70%), validation (15%), testing (15%) sets. paper shows beats standard methods in all four climatic scenarios, R² values 99.62%, 99.15%, 99.71%, 99.60%. Deep predicts climate well can guide environmental policy urban development decisions.

Language: Английский

Citations

0