Flood Hazard Rating Prediction for Urban Areas Using Random Forest and LSTM

2020 ◽  
Vol 24 (12) ◽  
pp. 3884-3896
Author(s):  
Hyun Il Kim ◽  
Byung Hyun Kim
Keyword(s):  
Random Forest ◽  
Urban Areas ◽  
Flood Hazard ◽  
Rating Prediction ◽  
Hazard Rating

scholarly journals Urban Flood Modeling Using 2D Shallow-Water Equations in Ouagadougou, Burkina Faso

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2120
Author(s):  
Gnenakantanhan Coulibaly ◽  
Babacar Leye ◽  
Fowe Tazen ◽  
Lawani Adjadi Mounirou ◽  
Harouna Karambiri
Keyword(s):  
Shallow Water ◽  
Shallow Water Equations ◽  
Urban Areas ◽  
Flood Hazard ◽  
Scientific Information ◽  
Roughness Coefficient ◽  
Flood Events ◽  
African Countries ◽  
Numerical Tool ◽  
2D Shallow Water Equations

Appropriate methods and tools accessibility for bi-dimensional flow simulation leads to their weak use for floods assessment and forecasting in West African countries, particularly in urban areas where huge losses of life and property are recorded. To mitigate flood risks or to elaborate flood adaptation strategies, there is a need for scientific information on flood events. This paper focuses on a numerical tool developed for urban inundation extent simulation due to extreme tropical rainfall in Ouagadougou city. Two-dimensional (2D) shallow-water equations are solved using a finite volume method with a Harten, Lax, Van Leer (HLL) numerical fluxes approach. The Digital Elevation Model provided by NASA’s Shuttle Radar Topography Mission (SRTM) was used as the main input of the model. The results have shown the capability of the numerical tool developed to simulate flow depths in natural watercourses. The sensitivity of the model to rainfall intensity and soil roughness coefficient was highlighted through flood spatial extent and water depth at the outlet of the watershed. The performance of the model was assessed through the simulation of two flood events, with satisfactory values of the Nash–Sutcliffe criterion of 0.61 and 0.69. The study is expected to be useful for flood managers and decision makers in assessing flood hazard and vulnerability.

scholarly journals Safer_RAIN: a Fast-Processing DEM-Based Algorithm for Pluvial Flood Hazard Assessment Across Large Urban Areas

2019 ◽  
Author(s):  
Attilio Castellarin ◽  
Caterina Samela ◽  
Simone Persiano ◽  
Stefano Bagli ◽  
Valerio Luzzi ◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Urban Areas ◽  
Flood Hazard ◽  
Flood Hazard Assessment ◽  
Fast Processing

scholarly journals The Assessment of Level of Flash Floods Threat of Urbanised Areas

2017 ◽  
Vol 65 (2) ◽  
pp. 519-526 ◽  
Author(s):  
Pavla Štěpánková ◽  
Miroslav Dumbrovský ◽  
Karel Drbal
Keyword(s):  
Czech Republic ◽  
Urban Areas ◽  
Flood Hazard ◽  
Flash Floods ◽  
Flood Risk Assessment ◽  
Entire Area ◽  
The Czech Republic ◽  
Eu Directive ◽  
Run Off ◽  
Technical Measures

Flash floods (or torrential rain flooding) is another type of flood hazard which has caused casualties and significant property damages. A methodology for identification of urban areas, which can potentially be burdened by that type of flood hazard, was proposed. This method, also called Method of Critical Points (CP), is a repeatable process able to identify areas, which are significant in terms of formation of surface run‑off and erosion. As addition to the preliminary flood risk assessment according to EU Directive 2007/60/ES on the Assessment and Management of Flood Risks, the presented methodology was applied for the entire area of the Czech Republic and the results are being used for the updating of non‑technical measures, e.g. urban planning. In the article, the principles of methodology of CP are described and results of the first application in the Czech Republic are presented, as well as possible interpretations of them.

scholarly journals FLOOD MAPPING USING RANDOM FOREST AND IDENTIFYING THE ESSENTIAL CONDITIONING FACTORS; A CASE STUDY IN FREDERICTON, NEW BRUNSWICK, CANADA

2020 ◽  
Vol V-3-2020 ◽  
pp. 609-615 ◽  
Author(s):  
M. Esfandiari ◽  
S. Jabari ◽  
H. McGrath ◽  
D. Coleman
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
New Brunswick ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Satellite Image ◽  
Machine Learning Algorithms ◽  
Slope Aspect ◽  
Flood Peak ◽  
Conditioning Factors

Abstract. Flood is one of the most damaging natural hazards in urban areas in many places around the world as well as the city of Fredericton, New Brunswick, Canada. Recently, Fredericton has been flooded in two consecutive years in 2018 and 2019. Due to the complicated behaviour of water when a river overflows its bank, estimating the flood extent is challenging. The issue gets even more challenging when several different factors are affecting the water flow, like the land texture or the surface flatness, with varying degrees of intensity. Recently, machine learning algorithms and statistical methods are being used in many research studies for generating flood susceptibility maps using topographical, hydrological, and geological conditioning factors. One of the major issues that researchers have been facing is the complexity and the number of features required to input in a machine-learning algorithm to produce acceptable results. In this research, we used Random Forest to model the 2018 flood in Fredericton and analyzed the effect of several combinations of 12 different flood conditioning factors. The factors were tested against a Sentinel-2 optical satellite image available around the flood peak day. The highest accuracy was obtained using only 5 factors namely, altitude, slope, aspect, distance from the river, and land-use/cover with 97.57% overall accuracy and 95.14% kappa coefficient.

scholarly journals Combined fluvial and pluvial urban flood hazard analysis: method development and application to Can Tho City, Mekong Delta, Vietnam

2015 ◽  
Vol 3 (8) ◽  
pp. 4967-5013 ◽  
Author(s):  
H. Apel ◽  
O. M. Trepat ◽  
N. N. Hung ◽  
D. T. Chinh ◽  
B. Merz ◽  
...  
Keyword(s):  
Hydrodynamic Model ◽  
Urban Areas ◽  
Large Scale ◽  
Method Development ◽  
Flood Hazard ◽  
Hazard Analysis ◽  
Mekong Delta ◽  
Rain Gauge ◽  
Convective Precipitation ◽  
Can Tho City

Abstract. Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas, and the predominantly sealed urban surface prevents infiltration and facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either fluvial or pluvial flood hazard, studies of combined fluvial and pluvial flood hazard are hardly available. Thus this study aims at the analysis of fluvial and pluvial flood hazard individually, but also at developing a method for the analysis of combined pluvial and fluvial flood hazard. This combined fluvial-pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as example. In this tropical environment the annual monsoon triggered floods of the Mekong River can coincide with heavy local convective precipitation events causing both fluvial and pluvial flooding at the same time. Fluvial flood hazard was estimated with a copula based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. Pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data, and a stochastic rain storm generator. Inundation was simulated by a 2-dimensional hydrodynamic model implemented on a Graphical Processor Unit (GPU) for time-efficient flood propagation modelling. All hazards – fluvial, pluvial and combined – were accompanied by an uncertainty estimation considering the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median) and the uncertainty by percentile maps. The results are critically discussed and ways for their usage in flood risk management are outlined.

Assessment of flood hazard mapping in urban areas using entropy weighting method: a case study in Hamadan city, Iran

2019 ◽  
Vol 67 (5) ◽  
pp. 1435-1449 ◽  
Author(s):  
Mehdi Sepehri ◽  
Hossein Malekinezhad ◽  
Seyed Zeynalabedin Hosseini ◽  
Ali Reza Ildoromi
Keyword(s):  
Urban Areas ◽  
Flood Hazard ◽  
Hazard Mapping ◽  
Weighting Method

scholarly journals Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

2020 ◽  
Vol 12 (21) ◽  
pp. 3568
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Flash Flood ◽  
Flood Hazard ◽  
Machine Learning Algorithms ◽  
Mitigation Strategies ◽  
Boosted Regression Tree ◽  
Flash Flooding ◽  
Flood Susceptibility ◽  
Susceptibility Modeling

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

scholarly journals Risk-Based Early Warning System for Pluvial Flash Floods: Approaches and Foundations

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 127 ◽  
Author(s):  
Julian Hofmann ◽  
Holger Schüttrumpf
Keyword(s):  
Information System ◽  
Early Warning ◽  
Urban Areas ◽  
Heavy Rainfall ◽  
Flood Hazard ◽  
Significant Risk ◽  
Early Warning Systems ◽  
Flash Floods ◽  
Water Levels ◽  
Heavy Rainfall Event

In times of increasing weather extremes and expanding vulnerable cities, a significant risk to civilian security is posed by heavy rainfall induced flash floods. In contrast to river floods, pluvial flash floods can occur anytime, anywhere and vary enormously due to both terrain and climate factors. Current early warning systems (EWS) are based largely on measuring rainfall intensity or monitoring water levels, whereby the real danger due to urban torrential floods is just as insufficiently considered as the vulnerability of the physical infrastructure. For this reason, this article presents a concept for a risk-based EWS as one integral component of a multi-functional pluvial flood information system (MPFIS). Taking both the pluvial flood hazard as well as the damage potential into account, the EWS identifies the urban areas particularly affected by a forecasted heavy rainfall event and issues object-precise warnings in real-time. Further, the MPFIS performs a georeferenced documentation of occurred events as well as a systematic risk analysis, which at the same time forms the foundation of the proposed EWS. Based on a case study in the German city of Aachen and the event of 29 May 2018, the operation principle of the integrated information system is illustrated.

scholarly journals Methodologies to study the surface hydraulic behaviour of urban catchments during storm events

2011 ◽  
Vol 63 (11) ◽  
pp. 2666-2673 ◽  
Author(s):  
M. Gómez ◽  
F. Macchione ◽  
B. Russo
Keyword(s):  
Urban Areas ◽  
Flood Hazard ◽  
Spatial Density ◽  
Hydraulic Efficiency ◽  
Storm Events ◽  
Accurate Analysis ◽  
Numerical Application ◽  
Urban Flood ◽  
Hydraulic Behaviour ◽  
Urban Catchments

A good knowledge of the hydraulic behaviour of an urban catchment and its surface drainage system is an essential requirement to guarantee traffic and pedestrian safety. In many cases, inlets have been situated according to spatial density criteria. Indeed a more rational location of inlets on urban catchments must be defined according to an accurate analysis of the relationship between street flow and inlet hydraulic efficiency. Moreover we lack specific hazard criteria in terms of the maximum acceptable flow depths and velocities on the streets that do not cause problems to pedestrians. In this paper the results of two different experimental campaigns are presented. The first was carried out to evaluate inlet hydraulic efficiency; the second was carried out to address the pedestrian stability in urban flood conditions, whose aim was to propose new hazard criteria. On the basis of the experimental results, a methodology was developed to assess flood hazard in urban areas during storm events. If a refined topographic representation of urban areas is available, a two-dimensional numerical simulation of urban flooding can be performed using complete shallow water equations. According to this approach a numerical application for flood hazard assessment in a street of Barcelona is shown.

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