Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A novel hybrid machine learning framework for spatio-temporal analysis of reference evapotranspiration in India
Study region: The study focuses on the diverse climatic regions of India, spanning arid, semi-arid, sub-humid, and humid zones. Study focus: This research employs a novel hybrid machine learning (ML) framework for precise spatio-temporal reference evapotranspiration (ETo) modelling from 1970 to 2024, addressing the variability in temperature, humidity, and precipitation. Three advanced ML models—Quantile-Adjusted xLSTM Network (QAxLNet), Quantile-Score Diffusion Model (QSDM), and Attentive Deep Quantile-Aware Autoencoder Network (ADAQNet)—are proposed and applied, focusing on relative humidity and temperature as critical predictors. Model validation, conducted with EEFlux-derived ETo data and Indian Meteorological Department (IMD) benchmarks, revealed strong alignment across diverse climatic zones. New hydrological insights for the region: The MPI-ESM1–2-HR model under SSP3–7.0 scenarios outperformed other CMIP6 models, with a correlation coefficient of 0.975 and spatial error of 3.55 mm. The ADAQNet demonstrated superior performance, with lowest errors RMSE (Train: 0.2247, Test: 0.2499), and R2 (Train: 0.96; Test: 0.9571) among the models. ETo declined at an average rate of 1.9 mm/year, indicating the role of climate change. ETo variability closely mirrored the spatial distribution of the National Building Code (NBC) of India. Seasonal variations were significant, with arid regions (Rajasthan, Gujarat) experiencing the highest increase (2.5–5.1 mm/year). Humid regions showed high sensitivity to RH forecasts, with up to 20 % ETo deviation. The study emphasizes the spatial, temporal, and seasonal variations of ETo across the region, highlighting its dependence on climatic factors.
A novel hybrid machine learning framework for spatio-temporal analysis of reference evapotranspiration in India
Study region: The study focuses on the diverse climatic regions of India, spanning arid, semi-arid, sub-humid, and humid zones. Study focus: This research employs a novel hybrid machine learning (ML) framework for precise spatio-temporal reference evapotranspiration (ETo) modelling from 1970 to 2024, addressing the variability in temperature, humidity, and precipitation. Three advanced ML models—Quantile-Adjusted xLSTM Network (QAxLNet), Quantile-Score Diffusion Model (QSDM), and Attentive Deep Quantile-Aware Autoencoder Network (ADAQNet)—are proposed and applied, focusing on relative humidity and temperature as critical predictors. Model validation, conducted with EEFlux-derived ETo data and Indian Meteorological Department (IMD) benchmarks, revealed strong alignment across diverse climatic zones. New hydrological insights for the region: The MPI-ESM1–2-HR model under SSP3–7.0 scenarios outperformed other CMIP6 models, with a correlation coefficient of 0.975 and spatial error of 3.55 mm. The ADAQNet demonstrated superior performance, with lowest errors RMSE (Train: 0.2247, Test: 0.2499), and R2 (Train: 0.96; Test: 0.9571) among the models. ETo declined at an average rate of 1.9 mm/year, indicating the role of climate change. ETo variability closely mirrored the spatial distribution of the National Building Code (NBC) of India. Seasonal variations were significant, with arid regions (Rajasthan, Gujarat) experiencing the highest increase (2.5–5.1 mm/year). Humid regions showed high sensitivity to RH forecasts, with up to 20 % ETo deviation. The study emphasizes the spatial, temporal, and seasonal variations of ETo across the region, highlighting its dependence on climatic factors.
A novel hybrid machine learning framework for spatio-temporal analysis of reference evapotranspiration in India
Dolon Banerjee (Autor:in) / Sayantan Ganguly (Autor:in) / Wen-Ping Tsai (Autor:in)
2025
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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