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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Predictive modeling of land surface temperature (LST) based on Landsat-8 satellite data and machine learning models for sustainable development

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 444, P. 141035 - 141035

Published: Feb. 8, 2024

Language: Английский

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Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates

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

Published: Jan. 20, 2025

Language: Английский

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Forecasting climate risk and heat stress hazards in arid ecosystems: Machine learning and ensemble models for specific humidity prediction in Dammam, Saudi Arabia

Natural Hazards, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 18, 2025

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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Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions

Pure and Applied Geophysics, Journal Year: 2024, Volume and Issue: 181(2), P. 719 - 747

Published: Feb. 1, 2024

Language: Английский

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Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100)

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 633, P. 130968 - 130968

Published: Feb. 28, 2024

Water availability for agricultural practices is dynamically influenced by climatic variables, particularly droughts. Consequently, the assessment of drought events directly related to strategic water management in sector. The application machine learning (ML) algorithms different scenarios variables a new approach that needs be evaluated. In this context, current research aims forecast short-term i.e., SPI-3 from predictors under historical (1901–2020) and future (2021–2100) employing (bagging (BG), random forest (RF), decision table (DT), M5P) Hungary, Central Europe. Three meteorological stations namely, Budapest (BD) (central Hungary), Szeged (SZ) (east south Szombathely (SzO) (west Hungary) were selected agriculture Standardized Precipitation Index (SPI-3) long run. For purpose, ensemble means three global circulation models GCMs CMIP6 are being used get projected time series indicators (i.e., rainfall R, mean temperature T, maximum Tmax, minimum Tmin two socioeconomic pathways (SSP2-4.5 SSP4-6.0). results study revealed more severe extreme past decades, which increase near (2021–2040). Man-Kendall test (Tau) along with Sen's slope (SS) also an increasing trend period Tau = −0.2, SS −0.05, −0.12, −0.09 SSP2-4.5 −0.1, −0.08 SSP4-6.0. Implementation ML scenarios: SC1 (R + T Tmax Tmin), SC2 (R), SC3 T)) at BD station RF-SC3 lowest RMSE RFSC3-TR 0.33, highest NSE 0.89 performed best forecasting on dataset. Hence, was implemented remaining (SZ SzO) 1901 2100 Interestingly, forecasted SSP2-4.5, 0.34 0.88 SZ 0.87 SzO SSP2-4.5. our findings recommend using provide accurate predictions R projections. This could foster gradual shift towards sustainability improve resources. However, concrete plans still needed mitigate negative impacts 2028, 2030, 2031, 2034. Finally, validation RF prediction large dataset makes it significant use other studies facilitates making disaster strategies.

Language: Английский

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Daily suspended sediment yield estimation using soft-computing algorithms for hilly watersheds in a data-scarce situation: a case study of Bino watershed, Uttarakhand

Theoretical and Applied Climatology, Journal Year: 2024, Volume and Issue: 155(5), P. 4023 - 4047

Published: Feb. 10, 2024

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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Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

Water, Journal Year: 2024, Volume and Issue: 16(13), P. 1904 - 1904

Published: July 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Language: Английский

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