Depth prediction of urban flood under different rainfall return periods based on deep learning and data warehouse

2020 ◽  
Vol 716 ◽  
pp. 137077 ◽  
Author(s):  
Zening Wu ◽  
Yihong Zhou ◽  
Huiliang Wang ◽  
Zihao Jiang
Keyword(s):  
Deep Learning ◽  
Data Warehouse ◽  
Return Periods ◽  
Urban Flood ◽  
Depth Prediction

Applying Deep Learning to Audit Procedures: An Illustrative Framework

2019 ◽  
Vol 33 (3) ◽  
pp. 89-109 ◽  
Author(s):  
Ting (Sophia) Sun
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Data Warehouse ◽  
Visual Recognition ◽  
Prediction Models ◽  
Historical Data ◽  
Structured Data ◽  
Text Understanding ◽  
Audit Data ◽  
Learning Functions

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.

scholarly journals Modelling and assessment of urban flood hazards based on rainfall intensity-duration-frequency curves reformation

2016 ◽  
Author(s):  
Reza Ghazavi ◽  
Ali Moafi Rabori ◽  
Mohsen Ahadnejad Reveshty
Keyword(s):  
Return Period ◽  
Urban Areas ◽  
Rainfall Intensity ◽  
Flood Hazards ◽  
Return Periods ◽  
Urban Flood ◽  
Design Storm ◽  
Hourly Rainfall ◽  
Idf Curves ◽  
Intensity Duration Frequency

Abstract. Estimate design storm based on rainfall intensity–duration–frequency (IDF) curves is an important parameter for hydrologic planning of urban areas. The main aim of this study was to estimate rainfall intensities of Zanjan city watershed based on overall relationship of rainfall IDF curves and appropriate model of hourly rainfall estimation (Sherman method, Ghahreman and Abkhezr method). Hydrologic and hydraulic impacts of rainfall IDF curves change in flood properties was evaluated via Stormwater Management Model (SWMM). The accuracy of model simulations was confirmed based on the results of calibration. Design hyetographs in different return periods show that estimated rainfall depth via Sherman method are greater than other method except for 2-year return period. According to Ghahreman and Abkhezr method, decrease of runoff peak was 30, 39, 41 and 42 percent for 5-10-20 and 50-year return periods respectively, while runoff peak for 2-year return period was increased by 20 percent.

scholarly journals Risk Assessment of Urban Floods Based on a SWMM-MIKE21-Coupled Model Using GF-2 Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4381
Author(s):  
Lidong Zhao ◽  
Ting Zhang ◽  
Jun Fu ◽  
Jianzhu Li ◽  
Zhengxiong Cao ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Return Period ◽  
Flood Risk ◽  
Global Climate ◽  
Total Loss ◽  
Return Periods ◽  
Rapid Urbanization ◽  
Urban Floods ◽  
Urban Flood ◽  
Inundation Depth

Global climate change and rapid urbanization have caused increases in urban floods. Urban flood risk assessment is a vital method for preventing and controlling such disasters. This paper takes the central region of Cangzhou city in Hebei Province as an example. Detailed topographical information, such as the buildings and roads in the study area, was extracted from GF-2 data. By coupling the two models, the SWMM and MIKE21, the spatial distribution of the inundation region, and the water depth in the study area under different return periods, were simulated in detail. The results showed that, for the different return periods, the inundation region was generally consistent. However, there was a large increase in the mean inundation depth within a 10-to-30-year return period, and the increase in the maximum inundation depth and inundation area remained steady. The comprehensive runoff coefficient in all of the scenarios exceeded 0.8, indicating that the drainage system in the study area is insufficient and has a higher flood risk. The flood risk of the study area was evaluated based on the damage curve, which was obtained from field investigations. The results demonstrate that the loss per unit area was less than CNY 250/m2 in each return period in the majority of the damaged areas. Additionally, the total loss was mainly influenced by the damaged area, but, in commercial areas, the total loss was highly sensitive to the inundation depth.

Semi-supervised image depth prediction with deep learning and binocular algorithms

2020 ◽  
Vol 92 ◽  
pp. 106272 ◽  
Author(s):  
Kuo-Kun Tseng ◽  
Yaqi Zhang ◽  
Qinglin Zhu ◽  
K.L. Yung ◽  
W.H. Ip
Keyword(s):  
Deep Learning ◽  
Depth Prediction ◽  
Image Depth

scholarly journals Effect of Independent Variables on Urban Flood Models

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3442 ◽  
Author(s):  
Yanfen Geng ◽  
Baohang Zhu ◽  
Xin Zheng
Keyword(s):  
Land Cover ◽  
Building Blocks ◽  
Model Verification ◽  
Relative Depth ◽  
Return Periods ◽  
Flood Events ◽  
Simulation Accuracy ◽  
Urban Flood ◽  
Independent Variables ◽  
Resolution Model

The simulation accuracy of urban flood models is affected by independent variables describing terrain resolution and artificial land cover. An evaluation of these effects could provide suggestions for the improvement of simulation accuracy when the available terrain resolutions and representation methods of land cover are different. This paper focused on exploring and evaluating these effects on simulation accuracy using two indicators, relative depth accuracy (RDA) and relative area accuracy (RAA). The study area was the Nanjing Jianye district in China, which has experienced extensive urbanization. Designed rainfall (2 and 10 year return periods) and three terrain resolutions (17, 35, and 70 m) were used in this paper. Building blocks (BB), road drainage (RD), and a combination of both (BB + RD) were compared to present the effect of artificial land cover. Real flood events were initially simulated as a model verification case, and hypothetic modeling scenarios were simulated to evaluate the effects of different resolutions and representation methods. The results indicate that the effect of terrain resolutions on simulation accuracy was more obvious than that of artificial land cover in the study area. In this paper, 20–30% higher accuracy could be achieved in the 35 m resolution model with respect to the 70 m resolution model. A relative accuracy of 94% was achieved in the 17 m resolution model when using the BB method, which was 5% higher than that using the RD method. This paper shows that evaluating the effects of terrain resolution and artificial land cover is effective and helpful for improving the simulation accuracy of urban flood models in extensively urbanized districts.

scholarly journals Building a Real-Time 2D Lidar Using Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Nadim Arubai ◽  
Omar Hamdoun ◽  
Assef Jafar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Tilt Angle ◽  
State Of The Art ◽  
The State ◽  
Prediction Problem ◽  
Learning Methods ◽  
Depth Prediction ◽  
Avoidance Problem

Applying deep learning methods, this paper addresses depth prediction problem resulting from single monocular images. A vector of distances is predicted instead of a whole image matrix. A vector-only prediction decreases training overhead and prediction periods and requires less resources (memory, CPU). We propose a module which is more time efficient than the state-of-the-art modules ResNet, VGG, FCRN, and DORN. We enhanced the network results by training it on depth vectors from other levels (we get a new level by changing the Lidar tilt angle). The predicted results give a vector of distances around the robot, which is sufficient for the obstacle avoidance problem and many other applications.

scholarly journals A deep learning technique-based automatic monitoring method for experimental urban road inundation

2021 ◽  
Author(s):  
Hao Han ◽  
Jingming Hou ◽  
Ganggang Bai ◽  
Bingyao Li ◽  
Tian Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Monitoring ◽  
Water Area ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
Validation Data ◽  
Monitoring Method ◽  
Urban Flood ◽  
Urban Road ◽  
Average Accuracy

Abstract Reports indicate that high-cost, insecurity, and difficulty in complex environments hinder the traditional urban road inundation monitoring approach. This work proposed an automatic monitoring method for experimental urban road inundation based on the YOLOv2 deep learning framework. The proposed method is an affordable, secure, with high accuracy rates in urban road inundation evaluation. The automatic detection of experimental urban road inundation was carried out under both dry and wet conditions on roads in the study area with a scale of few m2. The validation average accuracy rate of the model was high with 90.1% inundation detection, while its training average accuracy rate was 96.1%. This indicated that the model has effective performance with high detection accuracy and recognition ability. Besides, the inundated water area of the experimental inundation region and the real road inundation region in the images was computed, showing that the relative errors of the measured area and the computed area were less than 20%. The results indicated that the proposed method can provide reliable inundation area evaluation. Therefore, our findings provide an effective guide in the management of urban floods and urban flood-warning, as well as systematical validation data for hydrologic and hydrodynamic models.

scholarly journals A Method for Dynamical Sub-Watershed Delimitating by No-Fill Digital Elevation Model and Defined Precipitation: A Case Study of Wuhan, China

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 486
Author(s):  
Hongping Zhang ◽  
Xinwen Cheng ◽  
Lei Jin ◽  
Dong Zhao ◽  
Tianjing Feng ◽  
...  
Keyword(s):  
Water Level ◽  
Digital Elevation Model ◽  
Flood Control ◽  
Return Periods ◽  
Wuhan City ◽  
Urban Flood ◽  
Digital Elevation ◽  
Elevation Model ◽  
Control Management

Watershed delimitation is very important in flood control management. The traditional sub-watersheds delimitated by a filling digital elevation model (DEM) may change the real sink area, such that it may not be the best choice in studies sensitive to sub-watershed storage. This paper proposes a dynamical watershed delimitation method using a no-fill DEM and precipitation. It considers a closed sink area containing cells that fully flow into a large special cell, which can flow out when its water level is “higher than outlet”. We took Wuhan City as a study area and defined the precipitation in return periods of 1, 5, 20, or 100 years to derive the sub-watersheds. It is found that, in the four delimitations, the ratio of isolated basic units which could not flow outside were 27%, 9%, 5%, and 1%, respectively, as the precipitation increased. The results show that the provided method satisfies the assumption that the sink area might overflow with increased precipitation. The sub-watershed delimitated by the proposed method has higher correlation with the distribution of waterlogging points than those delimitated according to the D8 algorithm. These findings indicate that the proposed method can derive reasonable sub-watershed delimitation and that it may be helpful in the practice of urban flood control management.

scholarly journals Damage assessment of infrastructure based on urban flood inundation simulation

2020 ◽  
Vol 194 ◽  
pp. 05056
Author(s):  
Zhu Yifan ◽  
Yu Ping ◽  
Wang Peng
Keyword(s):  
Flood Damage ◽  
Flood Inundation ◽  
Return Periods ◽  
Runoff Simulation ◽  
Flood Simulation ◽  
Urban Flood ◽  
Event Simulation ◽  
Theoretical Support ◽  
Inundation Simulation ◽  
Inundation Map

Flood is a serious challenge that increasingly produce immense economical and ecological damages in worldwide. Flood simulation is gradually becoming a central focus of the hydrology in recent years. It is worthwhile to combine flood simulation with flood damage to infrastructure. The purpose of this study is to develop an approach for better urabn flood simulation at different return periods and assess the damage of flood to infrastructures in different return period. The approach is organized into four parts: design rainstorm event simulation, runoff simulation, flood inundation simulation and submerged infrastructures extraction. Base on the flood simulation, we draw the flood inundation map of Xuanwu-Qinhuai-Jianye-Gulou-Yuhuatai (XQJGY) region. Results indicated that the transportation infrastructures suffer the largest proportion of flood damage. This study simulates the inundated infrastructure at different return periods, provides a theoretical support for flood risk management.

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