Prediction of the standardized precipitation index based on the long short-term memory and empirical mode decomposition-extreme learning machine models: The Case of Sakarya, Türkiye

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2023, Volume and Issue: 131, P. 103418 - 103418

Published: May 18, 2023

Language: Английский

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Comparison of LSTM and SVM methods through wavelet decomposition in drought forecasting

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 1, 2025

Language: Английский

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A novel implementation of pre-processing approaches and hybrid kernel-based model for short- and long-term groundwater drought forecasting

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 652, P. 132667 - 132667

Published: Jan. 6, 2025

Language: Английский

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Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 22, 2025

Introduction Climate change isone of the major challenges facing world today, causing frequent extreme weather events that significantly impact human production, life, and ecological environment. Traditional climate prediction models largely rely on simulation physical processes. While they have achieved some success, these still face issues such as complexity, high computational cost, insufficient handling multivariable nonlinear relationships. Methods In light this, this paper proposes a hybrid deep learning model based Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve accuracy predictions. Firstly, Transformer is introduced capture complex patterns in cimate data time series through its powerful sequence modeling capabilities. Secondly, CNN utilized extract local features short-term changes. Lastly, LSTM adept at long-term dependencies, ensuring can remember utilize information over extended spans. Results Discussion Experiments conducted temperature from Guangdong Province China validate performance proposed model. Compared four different decomposition methods, with method performs best. The resuts also show Transformer-CNN-LSTM outperforms other five evaluation metrics, indicating provides more accurate predictions stable fitting results.

Language: Английский

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Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies

Sustainability, Journal Year: 2023, Volume and Issue: 15(15), P. 11684 - 11684

Published: July 28, 2023

Precipitation deficit conditions and temperature anomalies are responsible for the occurrence of various types natural disasters that cause tremendous loss human life economy country. Out all disasters, drought is one most recurring complex phenomenons. Prediction onset poses significant challenges to societies worldwide. Drought occurrences occur across world due a variety hydro-meteorological causes in sea surface temperature. This article aims provide comprehensive overview fundamental concepts characteristics drought, its nature, factors influence indicators, advanced prediction models. An extensive survey presented different models employed literature, ranging from statistical approaches machine learning deep It has been found techniques like outperform traditional by improving accuracy. review critically examines advancements technology have facilitated improved prediction, identifies key opportunities field trends topics likely give new directions future research. explores integration remote sensing data, meteorological observations, hydrological modeling, climate indices enhanced Under frequently changing conditions, this provides valuable resource researchers, practitioners, policymakers engaged management fosters deeper understanding their capabilities limitations. paves way more accurate effective strategies, contributing resilience sustainable development drought-prone regions.

Language: Английский

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Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 152(1-2), P. 535 - 558

Published: March 23, 2023

Language: Английский

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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 155(1), P. 1 - 44

Published: Aug. 28, 2023

Abstract Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency intensity of extremes other associated are continuously increasing due climate change global warming. accurate prediction, characterization, attribution atmospheric is, therefore, a key research field in which many groups currently working by applying different methodologies computational tools. Machine learning deep methods have arisen the last years as powerful techniques tackle problems related events. This paper reviews machine approaches applied analysis, most important extremes. A summary used this area, comprehensive critical review literature ML EEs, provided. has been extended rainfall floods, heatwaves temperatures, droughts, weather fog, low-visibility episodes. case study focused on analysis temperature prediction with DL is also presented paper. Conclusions, perspectives, outlooks finally drawn.

Language: Английский

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Prioritizing sub-watersheds for soil erosion using geospatial techniques based on morphometric and hypsometric analysis: a case study of the Indian Wyra River basin

Applied Water Science, Journal Year: 2023, Volume and Issue: 13(7)

Published: June 26, 2023

Abstract The hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform quantitative evaluation the watershed’s geometry determine impact such on its hydrology. Geographic information systems (GIS) remote sensing (RS) techniques have become increasingly significant because they enable decision-makers strategists make accurate efficient decisions. To prioritize sub-watersheds Wyra watershed, this research employs two methods: morphometric analysis hypsometric analysis. watershed was divided into eleven (SWs). prioritization in involved assessing several parameters, as relief, linear, areal features, for each sub-watershed. Furthermore, importance determined computing integral (HI) values using elevation–relief ratio method. final based through integration principal component (PCA) weighted sum approach (WSA). SW2 SW9 had higher priorities analysis, whereas SW6, SW7, SW10 obtained SW4 most common SW that shares same priority. vulnerable are those with highest priority, therefore, programmes soil conservation should pay more attention them. conclusions study may prove useful various stakeholders initiatives related development management.

Language: Английский

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Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

Environmental Pollution, Journal Year: 2024, Volume and Issue: 351, P. 124040 - 124040

Published: April 27, 2024

Language: Английский

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Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Heliyon, Journal Year: 2024, Volume and Issue: 10(10), P. e31085 - e31085

Published: May 1, 2024

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization industrialization. This study introduces Artificial Neural Networks (ANN) its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS Subspace), ANN-M5P (M5 Pruned), ANN-AR (Additive Regression) water the rapidly urbanizing industrializing Bagh River Basin, India. The Relief algorithm was employed to select most influential input parameters, including Nitrate (NO

Language: Английский

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