Adaptive meta-modeling of evapotranspiration in arid agricultural regions of Saudi Arabia using climatic factors, drought indices and MODIS data

Journal of Hydrology Regional Studies, Journal Year: 2025, Volume and Issue: 59, P. 102279 - 102279

Published: March 17, 2025

Language: Английский

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Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study

PLOS Water, Journal Year: 2025, Volume and Issue: 4(4), P. e0000359 - e0000359

Published: April 21, 2025

Streamflow plays a vital role in water resource management and environmental impact assessment. This study is novel application of the Long Short-Term Memory (LSTM) model, type recurrent neural network, for real-time streamflow prediction Upper Humber River Watershed western Newfoundland. It also compares performance LSTM model with physically based SWAT model. The was optimized by tuning hyperparameters adjusting window size to balance capturing historical data ensuring stability. Using single input variables such as daily average temperature or precipitation, achieved high Nash-Sutcliffe Efficiency (NSE) 0.95. In comparison, results show that delivers more competitive performance, achieving an NSE 0.95 versus SWAT’s 0.77, percent bias (PBIAS) 0.62 compared 8.26. Unlike SWAT, does not overestimate flows excels predicting low flows. Additionally, successfully predicted using data. Despite challenges interpretability generalizability, demonstrated strong particularly during extreme events, making it valuable tool cold climates where accurate forecasts are crucial effective management. highlights potential model’s

Language: Английский

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A Daily Reference Crop Evapotranspiration Forecasting Model Based on Improved Informer

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 933 - 933

Published: April 25, 2025

Daily reference crop evapotranspiration (ET0) is crucial for precision irrigation management, yet traditional prediction methods struggle to capture its dynamic variations due the complexity and nonlinearity of meteorological conditions. To address this, we propose an Improved Informer model enhance ET0 accuracy, providing a scientific basis agricultural water management. Using soil data from Yingde region, employed Maximal Information Coefficient (MIC) identify key influencing factors integrated Residual Cycle Forecasting (RCF), Star Aggregate Redistribute (STAR), Fully Adaptive Normalization (FAN) techniques into model. MIC analysis identified total shortwave radiation, sunshine duration, maximum temperature at 2 m, 28–100 cm depth, surface pressure as optimal features. Under five-feature scenario (S3), improved achieved superior performance compared Long Short-Term Memory (LSTM) original models, with MAE reduced 0.065 (LSTM: 0.637, Informer: 0.171) MSE 0.007 0.678, 0.060). The inference time was also by 31%, highlighting enhanced computational efficiency. effectively captures periodic nonlinear characteristics ET0, offering novel solution management significant practical implications.

Language: Английский

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An Overview of Evapotranspiration Estimation Models Utilizing Artificial Intelligence

Water, Journal Year: 2025, Volume and Issue: 17(9), P. 1384 - 1384

Published: May 4, 2025

Evapotranspiration (ET) has a significant role in various natural and human systems, such as water cycle balance, climate regulation, ecosystem health, agriculture, hydrological cycle, resource management, studies. Among approaches that are employed for estimating ET, the Penman–Monteith equation is known widely accepted reference approach. However, extensive data requirement of this method crucial challenge limits its usage, particularly data-scarce regions. Therefore, an alternative approach, artificial intelligence (AI) models have gained prominence evapotranspiration because their capacity to handle complicated relationships between meteorological variables loss processes. These leverage large datasets advanced algorithms provide accurate timely ET predictions. The current research aims review previous studies addressing application AI model modeling under four main categories: neuron-based, tree-based, kernel-based, hybrid models. results study indicated traditional like (PM) require input data, while AI-based offer promising alternatives due ability complex nonlinear relationships. Despite potential, face challenges overfitting, interpretability, inconsistent variable selection, lack integration with physical processes, highlighting need standardized configurations, better pre-processing techniques, incorporation remote sensing data.

Language: Английский

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Evaluating machine learning models and feature selection for reference evapotranspiration estimation in semi-arid regions: a case study in Doukkala, Morocco

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(6)

Published: May 10, 2025

Language: Английский

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