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hydraulicmodel

所属分类：人工智能/神经网络/深度学习
开发工具：Python
文件大小：0KB
下载次数：2
上传日期：2023-06-19 08:50:04
上传者：sh-1993

说明：通过2D浅水方程进行洪水建模，
(Flood modeling by 2D shallow water equation,)

文件列表:

LICENSE (35149, 2023-06-19)
complextopography.py (3264, 2023-06-19)
discretization.py (4134, 2023-06-19)
discretization_CellSize.py (3983, 2023-06-19)
evapotranspiration.py (3863, 2023-06-19)
fig/ (0, 2023-06-19)
fig/domain.png (951034, 2023-06-19)
fig/dw.png (1171641, 2023-06-19)
fig/fv.png (81512, 2023-06-19)
fig/li.png (474979, 2023-06-19)
hm.py (3101, 2023-06-19)
hm_flood.py (1651, 2023-06-19)
landusecover.py (1515, 2023-06-19)
overland_diffusive.py (40295, 2023-06-19)
overland_inertial.py (7925, 2023-06-19)
overland_ub.py (444, 2023-06-19)
precipitation.py (1030, 2023-06-19)
pyeto/ (0, 2023-06-19)
pyeto/__init__.py (1894, 2023-06-19)
pyeto/_check.py (1906, 2023-06-19)
pyeto/convert.py (1033, 2023-06-19)
pyeto/fao.py (27315, 2023-06-19)
pyeto/thornthwaite.py (4272, 2023-06-19)

# hydraulicmodel Flood modeling by 2D shallow water equation. Refer to Hunter et al (2005), Bates et al. (2010). Please refer to the following paper on the detailed implementation and results. ``` @Article{ouyang2022employment, author = {Mao Ouyang and Shunji Kotsuki and Yuka Ito and Tomochika Tokunaga}, journal = {Journal of Hydrology: Regional Studies}, title = {Employment of hydraulic model and social media data for flood hazard assessment in an urban city}, year = {2022}, issn = {2214-5818}, pages = {101261}, volume = {44}, doi = {10.1016/j.ejrh.2022.101261}, } ``` ## Diffusive wave approximation $\frac{\partial h ^{i, j}}{\partial t} = \frac{Q_x^{i-1, j} - Q_x^{i, j} Q_y^{i, j-1} - Q_y^{i, j}}{\Delta x \Delta y}$ $Q_x^{i, j} = \frac{h_{flow}^{5/3}}{n} \left( \frac{h^{i-1, j} - h^{i, j}}{\Delta x} \right) ^{1/2} \Delta y$ $Q_y^{i, j} = \frac{h_{flow}^{5/3}}{n} \left( \frac{h^{i-1, j} - h^{i, j}}{\Delta y} \right) ^{1/2} \Delta x$ ## Local inertail approaximation $\frac{\partial h ^{i, j}}{\partial t} = \frac{Q_x^{i-1, j} - Q_x^{i, j} Q_y^{i, j-1} - Q_y^{i, j}}{\Delta x \Delta y}$ $q_{t \Delta t} = \frac{q_t - gh_{flow, t} \Delta t \frac{\partial h_t}{\partial x}} {1 g n^2 \Delta t q_t / h_{flow, t}^{7/3}}$ ## Time steps in two methods - Diffusive wave approximation $\Delta t = \frac{\Delta x^2}{4} min \left( \frac{2n}{h_{flow}^{5/3}}|\frac{\partial h}{\partial x}|^{1/2}, \frac{2n}{h_{flow}^{5/3}}|\frac{\partial h}{\partial y}|^{1/2} \right)$ - Local inertial approximation $\Delta t = \alpha \frac{\Delta x}{\sqrt{g (h-z)_{max}}}$ ## Implementation of these two approximations for simulating the flood in Mobara city on October 25, 2019 Setting of the domain and boundary conditions ![domain](https://github.com/einoo/hydraulicmodel/blob/master/./fig/domain.png) Results of the maximum inundation area calculated by the diffusive wave approximation, compared with the flood extent reported by Chiba Office ![diffusive wave](https://github.com/einoo/hydraulicmodel/blob/master/./fig/dw.png) Results of the inundation range considering the flood recession by the local inertial approximation, compared with the flood extent estimated by Geospatial Information Authority of Japan (GSI). ![local inertial](https://github.com/einoo/hydraulicmodel/blob/master/./fig/li.png) ## Evolution of flood volume ![flood volume](https://github.com/einoo/hydraulicmodel/blob/master/./fig/fv.png)

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