Research on Multi-scale Urban Flood Model Based on Data Fusion

Author(s):  
Haocheng Huang ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Hao Wang
Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.


2021 ◽  
pp. 126854
Author(s):  
Hamid Darabi ◽  
Ali Torabi Haghighi ◽  
Omid Rahmati ◽  
Abolfazl Jalali Shahrood ◽  
Sajad Rouzbeh ◽  
...  

2021 ◽  
Author(s):  
Heiko Apel ◽  
Sergiy Vorogushyn ◽  
Mostafa Farrag ◽  
Nguyen Viet Dung ◽  
Melanie Karremann ◽  
...  

<p>Urban flash floods caused by heavy convective precipitation pose an increasing threat to communes world-wide due to the increasing intensity and frequency of convective precipitation caused by a warming atmosphere. Thus, flood risk management plans adapted to the current flood risk but also capable of managing future risks are of high importance. These plans necessarily need model based pluvial flood risk simulations. In an urban environment these simulations have to have a high spatial and temporal resolution in order to site-specific management solutions. Moreover, the effect of the sewer systems needs to be included to achieve realistic inundation simulations, but also to assess the effectiveness of the sewer system and its fitness to future changes in the pluvial hazard. The setup of these models, however, typically requires a large amount of input data, a high degree of modelling expertise, a long time for setting up the model setup and to finally run the simulations. Therefor most communes cannot perform this task.</p><p> In order to provide model-based pluvial urban flood hazard and finally risk assessments for a large number of communes, the model system RIM<em>urban</em> was developed. The core of the system consists of a simplified raster-based 2D hydraulic model simulating the urban surface inundation in high spatial resolution. The model is implemented on GPUs for massive parallelization. The specific urban hydrology is considered by a capacity-based simulation of the sewer system and infiltration on non-sealed surfaces, and flow routing around buildings. The model thus considers the specific urban hydrological features, but with simplified approaches. Due to these simplifications the model setup can be performed with comparatively low data requirements, which can be covered with open data in most cases. The core data required are a high-resolution DEM, a layer of showing the buildings, and a land use map.</p><p>The spatially distributed rainfall input can be derived local precipitation records, or from an analysis of weather radar records of heavy precipitation events. A catalogue of heavy rain storms all over Germany is derived based on radar observations of the past 19 years. This catalogue serves as input for pluvial risk simulations for individual communes in Germany, as well as a catalogue of possible extreme events for the current climate. Future changes in these extreme events will be estimated based on regional climate simulations of a ΔT (1.5°C, 2°C) warmer world.</p><p>RIM<em>urban</em> simulates the urban inundation caused by these events, as well as the stress on the sewer system. Based on the inundation maps the damage to residential buildings will be estimated and further developed to a pluvial urban flood risk assessment. Because of the comparatively simple model structure and low data demand, the model setup can be easily automatized and transferred to most small to medium sized communes in Europe and even beyond, if the damage estimation is modified. RIM<em>urban</em> is thus seen as a generally appölicable screening tool for urban pluvial flood risk and a starting point for adapted risk management plans.</p>


AIChE Journal ◽  
2013 ◽  
Vol 59 (8) ◽  
pp. 3119-3130 ◽  
Author(s):  
B. Sun ◽  
L. He ◽  
B.T. Liu ◽  
F. Gu ◽  
C.J. Liu

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