scholarly journals A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

2021 ◽  
Author(s):  
Xingyu Yan ◽  
Kui Xu ◽  
Wenqiang Feng ◽  
Jing Chen
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Emergency Management ◽  
Large Scale ◽  
Numerical Models ◽  
Risk Area ◽  
Urban Floods ◽  
Simulation Accuracy ◽  
Urban Flood ◽  
Water Accumulation

AbstractClimate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

Recent advances on bedform research and application: Process-based to machine learning

2021 ◽  
Author(s):  
Sanjay Giri ◽  
Amin Shakya ◽  
Mohamed Nabi ◽  
Suleyman Naqshband ◽  
Toshiki Iwasaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sediment Transport ◽  
Flow Resistance ◽  
Large Scale ◽  
Data Science ◽  
Numerical Models ◽  
Hybrid Approach ◽  
Water Model ◽  
Micro Scale ◽  
Cfd Models

<p>Evolution and transition of bedforms in lowland rivers are micro-scale morphological processes that influence river management decisions. This work builds upon our past efforts that include physics-based modelling, physical experiments and the machine learning (ML) approach to predict bedform features, states as well as associated flow resistance. We revisit our past works and efforts on developing and applying numerical models, from simple to sophisticated, starting with a multi-scale shallow-water model with a dual-grid technique. The model incorporates an adjustment of the local bed shear stress by a slope effect and an additional term that influences bedform feature. Furthermore, we review our work on a vertical two-dimensional model with a free surface flow condition. We explore the effects of different sediment transport approaches such as equilibrium transport with bed slope correction and a non-equilibrium transport with pick-up and deposition. We revisit a sophisticated three-dimensional Large Eddy Simulation (LES) model with an improved sediment transport approach that includes sliding, rolling, and jumping based on a Lagrangian framework. Finally, we discuss about bedform states and transition that are studied using laboratory experiments as well as a theory-guided data science approach that assures logical reasoning to analyze physical phenomena with large amounts of data. A theoretical evaluation of parameters that influence bedform development is carried out, followed by classification of bedform type by using a neural network model.</p><p>In second part, we focus on practical application, and discuss about large-scale numerical models that are being applied in river engineering and management practices. Such models are found to have noticeable inaccuracies and uncertainties associated with various physical and non-physical reasons. A key physical problem of these large-scale numerical models is related to the prediction of evolution and transition of micro-scale bedforms, and associated flow resistance. The evolution and transition of bedforms during rising and falling stages of a flood wave have a noticeable impact on morphology and flow levels in low-land alluvial rivers. The interaction between flow and micro-scale bedforms cannot be considered in a physics-based manner in large-scale numerical models due to the incompatibility between the resolution of the models and the scale of morphological changes. The dynamics of bedforms and the corresponding changes in flow resistance are not captured. As a way forward, we propse a hydrid approach that includes application of the CFD models, mentioned above, to generate a large amount of data in complement with field and laboratory observations, analysis of their reliability based on which developing a ML model. The CFD models can replicate bedform evolution and transition processes as well as associated flow resistance in physics-based manner under steady and varying flow conditions. The hybrid approach of using CFD and ML models can offer a better prediction of flow resistance that can be coupled with large-scale numerical models to improve their performance. The reseach is in progress.</p>

scholarly journals Identification of the High-Risk Area for Schistosomiasis Transmission in China Based on Information Value and Machine Learning—A Newly Data-Driven Modeling Try

2021 ◽  
Author(s):  
Yan-Feng Gong ◽  
Ling-Qian Zhu ◽  
Yin-Long Li ◽  
Li-Juan Zhang ◽  
Jing-Bo Xue ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Coupled Model ◽  
Information Value ◽  
Risk Area ◽  
Coupled Models ◽  
Lake Region ◽  
Risk Areas ◽  
And Control ◽  
Machine Learning Models

Abstract Objective Information value (IV) and machine learning models were used to analyze and predict the high-risk distribution of schistosomiasis, in order to provide scientific evidence for disease surveillance and control in China. Methods The local case distribution from schistosomiasis surveillance data in China between 2005 and 2019 was assessed based on 19 variables including climate, geography, and social economy. Seven models were built in three categories including IV, three machine learning models (logistic regression, LR; random forest, RF; generalized boosted model, GBM), and three coupled models (coupled model of information value and logistic regression, IV + LR; coupled model of information value and random forest, IV + RF; coupled model of information value and generalized boosted model, IV + GBM). Accuracy, AUC (area under the curve), and F1-score were used to evaluate the prediction performance of the models. The best model was selected to predict the risk distribution for schistosomiasis. Results IV + GBM had the highest prediction effect (accuracy = 0.878, AUC = 0.902, F1 = 0.920). The results of IV + GBM showed that the risk area for transmission comprised 4.66% of China, mainly distributed in the coastal regions of the middle and lower reaches of the Yangtze River, the Poyang Lake region, and the Dongting Lake region. Risk areas can be divided into low-risk (2.47%), medium-risk (1.35%), and high-risk (0.84%). High-risk areas are primarily distributed in eastern Changde, western Yueyang, northeastern Yiyang, middle Changsha of the Hunan Province, southern Jiujiang, northern Nanchang, northeastern Shangrao, eastern Yichun in Jiangxi Province, southern Jingzhou, southern Xiantao, middle Wuhan in Hubei Province, southern Anqing, northwestern Guichi, eastern Wuhu in Anhui Province, middle Meishan, northern Leshan, and the middle of Liangshan in Sichuan Province. Conclusions The risk of schistosomiasis transmission in China still exists, with high-risk areas relatively concentrated within regions. Coupled models of IV and machine learning provide for effective analysis and prediction, forming a scientific basis for surveillance and control within key areas.

scholarly journals Influence of Detailed Topography when Modeling Flows in Street Junction During Urban Flood

2012 ◽  
Vol 7 (5) ◽  
pp. 560-566 ◽  
Author(s):  
Pierre-Henri Bazin ◽  
◽  
Anne Bessette ◽  
Emmanuel Mignot ◽  
André Paquier ◽  
...  
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Numerical Models ◽  
Flow Section ◽  
Urban Flood ◽  
Eddy Viscosity Model ◽  
Image Velocimetry ◽  
Urban Topography ◽  
Scale Particle ◽  
The Impact

Floods in dense urban areas propagatemainly through the streets, where the flow can be locally affected by elements of urban topography. This study aims at assessing the need of integrating detailed topography in numerical models when simulating urban floods. Acoustic Doppler Velocimetry and Large Scale Particle Image Velocimetry measurements in an experimental three branch junction representing a city crossroad are used to calibrate a numerical model solving the 2D shallow water equations. A constant eddy viscosity model proves to be accurate enough to calculate velocity fields, but such model requires a fine calibration against experimental data. Simulations run with this calibrated model are performed to study the impact of obstacles and sidewalks representative of urban areas. It is found that obstacles located in the downstream branch can highly perturb the velocities distribution downstream of the junction, whereas obstacles located in the upstream branches have less influence. The presence of sidewalks results in reduced flow section and higher velocities, but additional effects occur within and downstream of the junction. Simulations presented here show the need of considering detailed topography and elements of urban furniture if local velocities have to be represented.

scholarly journals Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice

10.2196/20545 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e20545
Author(s):  
Paul J Barr ◽  
James Ryan ◽  
Nicholas C Jacobson
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Language Processing ◽  
Large Scale ◽  
Clinical Symptoms ◽  
Clinical Phenotype ◽  
Clinic Visit ◽  
Patient Reported ◽  
Audio Recordings ◽  
Clinic Visits

COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required—audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.

Emergency management system of urban waterlogging based on cloud computing platform and 3D visualization

2020 ◽  
Vol 39 (4) ◽  
pp. 5595-5608
Author(s):  
Deng Lianbing ◽  
Li Daming ◽  
Cai Zhiming
Keyword(s):  
Cloud Computing ◽  
Emergency Management ◽  
Early Warning ◽  
Management System ◽  
3D Visualization ◽  
Simulation Accuracy ◽  
Urban Flood ◽  
Computing Platform ◽  
Emergency Management System ◽  
Cloud Computing Platform

In recent years, the problem of urban waterlogging has been highly valued. The application of information technology and image simulation to emergency management of urban waterlogging can improve urban flood prevention and disaster reduction capabilities and reduce disaster losses. In this paper, the author analyze the emergency management system of urban waterlogging based on cloud computing platform and 3D visualization. Collect data through street monitoring and drones, re-analyze the collected images, and screen cities for easy waterlogging. Researchers can rely on the high-performance computing power of the system and the visualized integrated environment to achieve online monitoring and early warning of waterlogging and 3D visual display. The system can provide early warning services in the form of alarms for monitoring results that exceed the threshold, and use mobile agents to send messages to relevant personnel in a variety of ways, providing fast auxiliary decision-making services. The simulation results show that the system has high simulation accuracy and can provide fast and efficient emergency services.

scholarly journals Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 722
Author(s):  
Mahaly Baptiste ◽  
Sarah Shireen Moinuddeen ◽  
Courtney Lace Soliz ◽  
Hashimul Ehsan ◽  
Gen Kaneko
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Genetic Information ◽  
Large Scale ◽  
Sequence Data ◽  
Precision Oncology ◽  
Nucleotide Polymorphisms ◽  
Making Sense ◽  
High Risk Populations ◽  
Systematic Identification

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

Weather data modelling for energy system models using machine learning

2020 ◽  
Author(s):  
Alexander Kies ◽  
Nishtha Srivastava ◽  
Kai Zhou ◽  
Jan Steinheimer ◽  
Horst Stoecker
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Numerical Models ◽  
Energy Systems ◽  
Energy System ◽  
Weather Data ◽  
Generative Adversarial Networks ◽  
System Models ◽  
Wind Speeds ◽  
Adversarial Networks

<p>Weather data is essential to model and optimise energy systems, which are based on high shares of renewable generation sources. However, differences between data sources can be significant and often little emphasis is put on energy-related variables such as hub-height wind speeds.</p><p>In this work, we use generative adversarial networks (GAN), a class of machine learning systems, to model weather data for large-scale energy system models and optimise energy systems of different scales and sizes.</p><p>We show that generating weather data using GAN saves effort as required for processing large amounts of weather data and that it can reliably reproduce results from using weather data produced by numerical models.</p>

scholarly journals A Framework for Risk-Based Assessment of Urban Floods in Coastal Cities

2021 ◽  
Author(s):  
M. Dinesh Kumar ◽  
Shubham Tandon ◽  
Nitin Bassi ◽  
Pradipta Kumar Mohanty ◽  
Saurabh Kumar ◽  
...  
Keyword(s):  
High Risk ◽  
Flood Risk ◽  
Risk Index ◽  
Progressive Increase ◽  
Urban Floods ◽  
Urban Flood ◽  
Coastal Cities ◽  
Index Value ◽  
The City ◽  
Urban Flood Risk

Abstract Many coastal cities in developing countries are at the risk of flooding due to a progressive increase in the built-up areas and poor management of stormwater. The flooding situation in coastal cities gets accentuated further due to climate induced natural disasters such as cyclones and climate change induced sea-level rise that adversely impact the city’s natural drainage potential. This study developed a composite urban flood risk index consisting of three sub-indices and 20 key natural, physical, social, and economic influencing variables for a coastal city (i.e. Cuttack) in eastern India, the intensity of storm runoff being one among the many. The intensity-duration-frequency curve developed shows that the city can experience floods with a peak discharge of 1,320 cubic metre per second every alternate year for a rainfall intensity of 2-hour duration. The urban flood risk index computed for all the city wards shows that out of the 59 wards, only one ward has low flood risk (index value < 0.40) and 20 wards are at high risk (index value 0.55 and above) from the urban flood. Thereafter, factors leading to high risk due to urban floods were identified and the institutional capacities available with the urban utility for fighting floods analyzed.

scholarly journals A Novel Machine Learning Strategy Based on Two-Dimensional Numerical Models in Financial Engineering

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Qingzhen Xu
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Large Scale ◽  
Numerical Solutions ◽  
Learning Strategy ◽  
Numerical Models ◽  
Relevant Information ◽  
Future Trend ◽  
Two Dimensional ◽  
Stock Market Index

Machine learning is the most commonly used technique to address larger and more complex tasks by analyzing the most relevant information already present in databases. In order to better predict the future trend of the index, this paper proposes a two-dimensional numerical model for machine learning to simulate major U.S. stock market index and uses a nonlinear implicit finite-difference method to find numerical solutions of the two-dimensional simulation model. The proposed machine learning method uses partial differential equations to predict the stock market and can be extensively used to accelerate large-scale data processing on the history database. The experimental results show that the proposed algorithm reduces the prediction error and improves forecasting precision.

Sign in / Sign up

Export Citation Format

Share Document