相关实验视频

Updated: May 26, 2025

Measurement of Leaf Hydraulic Conductance and Stomatal Conductance and Their Responses to Irradiance and Dehydration Using the Evaporative Flux Method EFM

Measurement of Leaf Hydraulic Conductance and Stomatal Conductance and Their Responses to Irradiance and Dehydration Using the Evaporative Flux Method EFM

Published on: December 31, 2012

在摩洛哥,以物理为基础的神经网络用于增强的参考蒸发透气估计:平衡半物理模型和深度学习

Chouaib El Hachimi¹, Salwa Belaqziz², Saïd Khabba³

¹Center for Remote Sensing Applications (CRSA), Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco; Department of Biological and Agricultural Engineering, University of California, Davis, CA, 95616, USA.

|February 21, 2025

概括

基于物理学的神经网络 (PINNs) 通过将半物理模型集成到AI中来改善参考蒸发透气 (ETo) 估计. 这提高了更好的农业水资源管理的准确性和可靠性.

科学领域:

环境科学环境科学
农业工程农业工程
人工智能的人工智能

背景情况:

参考蒸发透气 (ETO) 对农业至关重要,但像Penman-Monteith方程这样的标准方法是数据密集的.
现有的简化模型有局限性,纯粹基于数据的AI方法引发了对可解释性和可靠性的担忧.
需要准确,可靠和数据效率高的ETO估计技术.

研究的目的:

评估半物理 (SP) 模型的整合到物理信息神经网络 (PINN) 的损失函数中,用于ETO估计.
评估结合数据驱动和基于物理的损失对各种数据可用性场景中的模型性能的影响.
提高AI模型的可解释性和可靠性,用于环境应用中的ETO估计.

主要方法:

开发了一种新的PINN方法,将数据驱动的损失与使用凸组合参数 (θ) 的SP模型 (Priestley-Taylor,Makkink,Hargreaves-Samani,Abtew) 的损失结合起来.
从摩洛哥收集了现场农气数据 (空气温度,太阳辐射,相对湿度,风速).
使用CMA-ES优化校准SP模型,并在不同的数据场景和θ值下训练/评估PINN.

主要成果:

综合PINN方法在所有测试的数据场景中显著提高了ETO估计准确性 (RMSE,R2) 与完全数据驱动或基线模型相比.
当 θ 在 [0.2,0.8] 范围内时,PINN 需要更短的训练时间来达到最佳值.
确定了每个SP模型和数据场景的最佳θ值,证明了方法的灵活性.

结论:

基于物理学的神经网络为参考蒸发透气估计提供了一个有希望,准确和值得信赖的方法.
将物理约束集成到AI模型中可以提高它们在运营环境管理中的可靠性和适用性.
这项研究代表了向开发可控,基于物理知识的环境科学应用的人工智能迈出的重要一步.

关键词:

水文学水文学灌灌方式半物理模型半物理模型可靠的人工智能可靠的人工智能

更多相关视频

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

相关概念视频

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Adaptations that Reduce Water Loss

Adaptations that Reduce Water Loss

Though evaporation from plant leaves drives transpiration, it also results in loss of water. Because water is critical for photosynthetic reactions and other cellular processes, evolutionary pressures on plants in different environments have driven the acquisition of adaptations that reduce water loss.