Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management-Reference-Cited by-同舟云学术

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Machine Learning vs. Physics-Based Modeling for Real-Time Irrigation Management

Published:2020-04-21 Issue: Volume:2 Page:
ISSN:2624-9375
Container-title:Frontiers in Water
language:
Short-container-title:Front. Water

Author:

Gumiere Silvio J.,Camporese Matteo,Botto Anna,Lafond Jonathan A.,Paniconi Claudio,Gallichand Jacques,Rousseau Alain N.

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Frontiers Media SA

Reference41 articles.

1. A new aerobiological indicator to optimize the prediction of the olive crop yield in intensive farming areas of southern Spain;Aguilera;Agric. For. Meteorol.,2019

2. Crop evapotranspiration - guidelines for computing crop water requirements;Allen,1998

3. Predicting soil and plant water status dynamic in olive orchards under different irrigation systems with Hydrus-2D: model performance and scenario analysis;Autovino;Agric. Water Manag.,2018

4. Estimation of latent evaporation from simple weather observations;Baier;Can. J. Plant Sci.,1965

5. Development of a steady-state model to predict daily water table depth and root zone soil matric potential of a cranberry field with a subirrigation system;Bigah;Agric. Water Manag.,2019

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2. Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms;Water;2024-05-11

3. Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models;Water Research X;2024-05

4. Application of machine learning approaches in supporting irrigation decision making: A review;Agricultural Water Management;2024-04

5. Harnessing the power of transformers and data fusion in smart irrigation;Applied Soft Computing;2024-02

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