Probing the limit of hydrologic predictability with the Transformer network

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 637, P. 131389 - 131389

Published: May 19, 2024

Language: Английский

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Evaluation of TerraClimate gridded data in investigating the changes of reference evapotranspiration in different climates of Iran

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 52, P. 101678 - 101678

Published: Jan. 28, 2024

Different climates in Iran, including the northwestern regions (cold), southern coasts (hot and humid) central dry). TerraClimate network data (5 km resolution) from 40 years (1981–2020) was used to investigate changes trend breaking point of reference crop evapotranspiration (ETo) time series for different Iran. Statistical assessment indices (RMSE, R2, BIAS) were initially employed assess accuracy with ground stations. The temporal-spatial alterations trend, ETo, its extreme values then investigated using Mann-Kendall, Sen's Slope, Pettitt, Bayesian quantile regression tests. correlation coefficient between monthly gridded ETo calculated be more than 0.95, an average value BIAS –1.1. studied had increased. increasing tendency high highest slopes cold winter (slope >70 %), hot/dry spring summer hot/humid 50 %). Additionally, findings Pettitt's test suggest that three Iran a rising break autumn (hot/dry), (hot/humid) seasons, respectively. This indicates rapid have been common 1996 2000.

Language: Английский

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Deep dive into predictive excellence: Transformer's impact on groundwater level prediction

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 636, P. 131250 - 131250

Published: April 28, 2024

Language: Английский

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Improving multi-month hydrological drought forecasting in a tropical region using hybridized extreme learning machine model with Beluga Whale Optimization algorithm

Stochastic Environmental Research and Risk Assessment, Journal Year: 2023, Volume and Issue: 37(12), P. 4963 - 4989

Published: Sept. 9, 2023

Language: Английский

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Attention is all water need: Multistep time series irrigation water demand forecasting in irrigation disctrics

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 218, P. 108723 - 108723

Published: Feb. 10, 2024

Energy demand, energy cost and water scarcity are three of the main problems in new Irrigation districts (ID) where irrigation distribution networks pressurized frequently organized on-demand (water is continuously available to farmers). This generates a huge degree uncertainty for IDs managers when it comes managing both use therefore its cost. Knowledge demand several days advance would facilitate management system help optimize costs. The artificial intelligence (AI) especially deep learning, developing forecasting model with capacity learn autonomously from real information each ID, key element achieve water-energy optimization. Previous works have successfully forecast at ahead. However, all these previous were based on human memory which fallible not easily pass operator next. In this work, combined modified version Transformer Neural Networks (TNNs), fuzzy logic Genetic Algorithms (GAs) middle-term time resolution (one-week ahead) ID scale has been developed tested working ID. improves representativeness accuracy best previously by 6.1 % 89.8 %, respectively. addition, only 9 attention heads 1.75 million parameters (only 16.7 denser than works) IWD was able 99.9 scenarios an average standard error prediction 2.10 %.

Language: Английский

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Hybridization of deep learning, nonlinear system identification and ensemble tree intelligence algorithms for pan evaporation estimation

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 640, P. 131704 - 131704

Published: July 20, 2024

Language: Английский

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Estimation and Spatiotemporal Evolution Analysis of Actual Evapotranspiration in Turpan and Hami Cities Based on Multi-Source Data

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(10), P. 2565 - 2565

Published: May 14, 2023

The accurate inversion of actual evapotranspiration (ETa) at a regional scale is crucial for understanding water circulation, climate change, and drought monitoring. In this study, we produced 1 km monthly ETa dataset Turpan Hami, two typical arid cities in northwest China, using multi-source remote sensing data, reanalysis information, the ETMonitor model from 1980 to 2021. We analyzed spatiotemporal variation various statistical approaches discussed impact land use cover changes (LUCC) on ETa. results show following: (1) estimation correlate well with products scales (coefficient determination (R2) > 0.85, root mean square error (RMSE) < 15 mm/month) high reliability. (2) values were spatially distributed similarly precipitation LUCC, multi-year (1980–2021) average 66.31 mm slightly fluctuating downward trend (−0.19 mm/a). (3) During 42-year period, 63.16% study area exhibited an insignificant decrease ETa, while 86.85% experienced pronounced fluctuations (CV) 0.20), 78.83% will upward future. (4) was significantly positively correlated (94.17%) insignificantly temperature (55.81%). human activities showed decreasing (85.41%). Additionally, intensity varied considerably among types, largest cropland (424.12 have implications promoting rational allocation resources improving efficiency zones.

Language: Английский

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Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Oct. 25, 2023

This study aims to assess the practicality of utilizing artificial intelligence (AI) replicate adsorption capability functionalized carbon nanotubes (CNTs) in context methylene blue (MB) removal. The process generating involved pyrolysis acetylene under conditions that were determined be optimal. These included a reaction temperature 550 °C, time 37.3 min, and gas ratio (H2/C2H2) 1.0. experimental data pertaining MB on CNTs was found extremely well-suited Pseudo-second-order model, as evidenced by an R2 value 0.998, X2 5.75, qe 163.93 (mg/g), K2 6.34 × 10-4 (g/mg min).The system exhibited best agreement with Langmuir yielding 0.989, RL 0.031, qm 250.0 mg/g. results AI modelling demonstrated remarkable performance using recurrent neural network, achieving highest correlation coefficient = 0.9471. Additionally, feed-forward network yielded 0.9658. modeling hold promise for accurately predicting capacity CNTs, which can potentially enhance their efficiency removing from wastewater.

Language: Английский

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Reservoir evaporation prediction with integrated development of deep neural network models and meta-heuristic algorithms (Case study: Dez Dam)

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: Jan. 27, 2025

Language: Английский

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Mixture of experts leveraging informer and LSTM variants for enhanced daily streamflow forecasting

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132737 - 132737

Published: Jan. 1, 2025

Language: Английский

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