Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting

Troncoso-García, A.R.; Brito, I.S.; Troncoso, A.; Martínez-Álvarez, F.

doi:10.1016/j.compag.2023.108387

Cited by 11 publications

(1 citation statement)

References 35 publications

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“…Troncoso-García et al employed historical evapotranspiration values as inputs to optimize a recursive long-short-term memory neural network using the Coronavirus Optimization Algorithm (CVOA) bio-heuristic algorithm. Demonstrating higher ET0 prediction accuracy and significantly reduced computational time compared to traditional methods, this model stands as the current leading approach for predicting reference evapotranspiration [133].…”

Section: Evapotranspiration Predictionmentioning

confidence: 99%

Research on Water Resource Modeling Based on Machine Learning Technologies

Liu,

Zhou,

Yang

et al. 2024

Water

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Water resource modeling is an important means of studying the distribution, change, utilization, and management of water resources. By establishing various models, water resources can be quantitatively described and predicted, providing a scientific basis for water resource management, protection, and planning. Traditional hydrological observation methods, often reliant on experience and statistical methods, are time-consuming and labor-intensive, frequently resulting in predictions of limited accuracy. However, machine learning technologies enhance the efficiency and sustainability of water resource modeling by analyzing extensive hydrogeological data, thereby improving predictions and optimizing water resource utilization and allocation. This review investigates the application of machine learning for predicting various aspects, including precipitation, flood, runoff, soil moisture, evapotranspiration, groundwater level, and water quality. It provides a detailed summary of various algorithms, examines their technical strengths and weaknesses, and discusses their potential applications in water resource modeling. Finally, this paper anticipates future development trends in the application of machine learning to water resource modeling.

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Section: Evapotranspiration Predictionmentioning

confidence: 99%