Attention-based convolutional capsules for evapotranspiration estimation at scale
Attention-based convolutional capsules for evapotranspiration estimation at scale
复制标题
基于注意力的卷积胶囊用于大规模蒸散发估计
DOI：
10.1016/j.envsoft.2022.105366
复制
发表时间：
2022
影响因子：
4.9
通讯作者：
Pallickara, Sangmi Lee
中科院分区：
文献类型：
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作者：
Armstrong, Samuel;Khandelwal, Paahuni;Padalia, Dhruv;Senay, Gabriel;Schulte, Darin;Andales, Allan;Breidt, F. Jay;Pallickara, Shrideep;Pallickara, Sangmi Lee
蒸散量（ET）测量从地球表面流失到大气中的水量，是农业和环境科学不可或缺的指标。了解和量化ET对于实现淡水和灌溉系统的有效管理至关重要。然而，目前的ET估计模型遭受的准确性和空间覆盖之间的权衡。在这项研究中，我们介绍了我们的模型Quench，这是一种神经网络架构，可以在大的连续空间范围内实现高度准确的ET估计。Quench将我们的新型基于注意力的卷积胶囊用于其神经网络层，以识别重点区域并有效地从卫星图像中提取ET信息。分析我们模型性能的基准测试显示，准确性有了实质性的提高，与传统的基于卷积和基于过程的模型相比，准确性提高了128%。Quench还展示了在高地理空间变异性和各种区域，季节，气候和植被上的一致模型性能。
Evapotranspiration (ET) measures the amount of water lost from the Earth's surface to the atmosphere and is an integral metric for both agricultural and environmental sciences. Understanding and quantifying ET is critical for achieving effective management of freshwater and irrigation systems. However, current ET estimation models suffer from a trade-off between accuracy and spatial coverage. In this study, we introduce our model Quench, a neural network architecture that achieves highly-accurate ET estimates over large continuous spatial extents. Quench uses our novel Attention-Based Convolutional Capsule for its neural network layers to identify areas of focus and efficiently extract ET information from satellite imagery. Benchmarks that profile our model's performance show substantive improvements in accuracy, with up to 128% increase in accuracy compared to traditional convolutional-based and process-based models. Quench also demonstrates consistent model performance over high geospatial variability and a diverse array of regions, seasons, climates, and vegetations.
DOI：
10.1016/j.envsoft.2020.104856
发表时间：
2020-09
期刊：
Environ. Model. Softw.
影响因子：
--
作者：
M. Sadeghi;P. Nguyen;K. Hsu;S. Sorooshian
通讯作者：
M. Sadeghi;P. Nguyen;K. Hsu;S. Sorooshian
科罗拉多州东南部精密称重蒸渗仪的设计和建造
DOI：
--
发表时间：
2018
期刊：
影响因子：
--
作者：
A. Andales;Dale Straw;T. Marek;Lane H Simmons;M. Bartolo;T. Ley
通讯作者：
T. Ley