Literature

里程碑文献

<p>提出了物理-大数据联合驱动的通用深度过程学习技术</p>

Leilei He, Liangsheng Shi*, Wenxiang Song, Jiawen Shen, Lijun Wang, Xiaolong Hu, Yuanyuan Zha. Synergizing Intuitive Physics and Big Data in Deep Learning: Can We Obtain Process Insights While Maintaining State-of-the-Art Hydrological Prediction Capability? Water Resources Research, 2024.

提出了物理-大数据联合驱动的通用深度过程学习技术

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<p>提出了物理-大数据联合驱动的通用深度过程学习技术</p>
<p>综述了地球科学中数据驱动的方程发现</p>

Wenxiang Song, Shijie Jiang, Gustau Camps-Valls, Mathew Williams, Lu Zhang, Markus Reichstein, Harry Vereecken, Leilei He, Xiaolong Hu, Liangsheng Shi*.
Towards data-driven discovery of governing equations in geosciences.
Communications Earth & Environment, 2024.

综述了地球科学中数据驱动的方程发现

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<p>综述了地球科学中数据驱动的方程发现</p>
<p>提出了一种复杂过程的物理信息神经网络</p>

Yanling Wang,Liangsheng Shi*, Xiaolong Hu, Wenxiang Song, Lijun Wang.
Multiphysics-informed neural networks for coupled soil hydrothermal modeling.
Water Resources Research, 2023.

提出了一种复杂过程的物理信息神经网络

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<p>提出了一种复杂过程的物理信息神经网络</p>
<p>以视觉的方式来研究岩石优先流通道</p>

Zhengkun Zhou, Liangsheng Shi*, Yuanyuan Zha, et al.
Densely connected squeeze-and-excitation convolutional encoder-decoder networks for identifying preferential channels in highly heterogeneous porous media.
Water Resources Research, 2022.

以视觉的方式来研究岩石优先流通道

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<p>以视觉的方式来研究岩石优先流通道</p>
<p>从数据中发掘水动力学方程</p>

Wenxiang Song, Liangsheng Shi*, Lijun Wang, Yanling Wang, Xiaolong Hu.
Data-driven discovery of soil moisture flow governing equation: a sparse regression framework.
Water Resources Research, 2022.

从数据中发掘水动力学方程

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<p>从数据中发掘水动力学方程</p>
<p>利用机器学习方法提高蒸散发估计精度</p>

Xiaolong Hu, Liangsheng Shi*, Lin Lin, Vincenzo Magliulo.
Improving surface roughness lengths estimation using machine learning algorithms.
Agricultural and Forest Meteorology, 2020.

利用机器学习方法提高蒸散发估计精度

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<p>利用机器学习方法提高蒸散发估计精度</p>
<p>开发了近实时深度学习方法,从无人机图像中<br />诊断作物生理特征</p>

Qi Yang, Liangsheng Shi*, Jingye Han, Jin Yu, Kai Huang.
A near real-time deep learning approach for detecting rice phenology based on UAV images.
Agricultural and Forest Meteorology, 2020.

开发了近实时深度学习方法,从无人机图像中
诊断作物生理特征

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<p>开发了近实时深度学习方法,从无人机图像中<br />诊断作物生理特征</p>
<p>发现了地表温度-植被指数空间的非线性边界,<br />精准评估区域干旱</p>

Xiaolong Hu, Liangsheng Shi*, Lin Lin, Yuanyuan Zha.
Nonlinear boundaries of land surface temperature-vegetation index space to estimate water deficit index and evaporation fraction.
Agricultural and Forest Meteorology, 2019.

发现了地表温度-植被指数空间的非线性边界,
精准评估区域干旱

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<p>发现了地表温度-植被指数空间的非线性边界,<br />精准评估区域干旱</p>
<p>如何更好地利用光学和热红外数据估计蒸散发</p>

Xiaolong Hu, Liangsheng Shi*, Lin Lin, Baozhong Zhang, Yuanyuan Zha.
Optical-based and thermal-based surface conductance and actual evapotranspiration estimation, an evaluation study in the North China Plain.
Agricultural and Forest Meteorology,
263, 449-464, 2018.

如何更好地利用光学和热红外数据估计蒸散发

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<p>如何更好地利用光学和热红外数据估计蒸散发</p>
<p>大型地下水问题的水力层析技术</p>

Yuanyuan Zha, Tian-Chyi J. Yeh, Walter Illman, Wenzhi Zeng, Yonggen Zhang, Fangqiang Sun,Liangsheng Shi*.
A Reduced-Order Successive Linear Estimator for Geostatistical Inversion and Its Application in Hydraulic Tomography.
Water Resources Research. 2018.

大型地下水问题的水力层析技术

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<p>大型地下水问题的水力层析技术</p>
<p>下一代灌区的理论和技术体系</p>

史良胜*, 查元源, 胡小龙, 杨琦.
智慧灌区的架构、理论和方法之初探.
水利学报, 2020, 51(10): 1212-1222.

下一代灌区的理论和技术体系

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<p>下一代灌区的理论和技术体系</p>