scholarly journals FLOOD HAZARD ASSESSMENT AND DELIMITATION OF THE LIKELY FLOOD HAZARD ZONES OF THE UPPER PART IN GALLIKOS RIVER BASIN

2017 ◽  
Vol 50 (2) ◽  
pp. 995 ◽  
Author(s):  
I. Tsitroulis ◽  
K. Voudouris ◽  
A. Vasileiou ◽  
C. Mattas ◽  
Μ. Sapountzis ◽  
...  
Keyword(s):  
River Basin ◽  
Prediction Models ◽  
Flood Hazard ◽  
Human Life ◽  
Meteorological Data ◽  
Flood Management ◽  
Flood Prediction ◽  
Management Framework ◽  
Inundation Maps ◽  
Software Packages

Floods are one of the most common natural hazards in global range and could threat the human life, health, environment and infrastructure. The aim of this paper is the estimation and the delimitation of the likely flood hazard zones, for different rainfall intensities in the upper part of Gallikos river basin (central Macedonia) according to the European directive 2007/60. For the analysis of the meteorological data and the construction of flood zone maps, HYDROGNOMON, HEC-HMS, HEC-RAS free software packages were used. The thematic maps were constructed with ESRI GIS. The results are depicted in flood inundation maps, delimitating and visualizing the scale of the flood hazard effect in the area. The construction of flood prediction models is a very useful tool towards the direction of the design of an efficient flood management framework.

scholarly journals Identification of Flood-prone Areas using HEC-HMS and HEC-RAS, the Case of Ciberang River Basin, Lebak District of Banten Province

2021 ◽  
Vol 930 (1) ◽  
pp. 012082
Author(s):  
Ynaotou ◽  
R Jayadi ◽  
A P Rahardjo ◽  
D A Puspitosari
Keyword(s):  
River Basin ◽  
Human Life ◽  
Control Point ◽  
Flood Damage ◽  
Level Gauge ◽  
Flood Hazards ◽  
Channel Routing ◽  
Flood Prediction ◽  
Discharge Data ◽  
Flood Prone Areas

Abstract It is common practice that flood hydrograph simulations help to provide better flood prediction and flood damage reduction planning. These efforts require information on flood-prone areas identification from the hydrological and hydraulic analysis results. Historically, the Ciberang River Basin has experienced floods. Those floods cause the loss of human life and damage some houses along the river’s channels, especially in Lebak District, Banten Province, Indonesia. The main objective of this study is to identify flood-prone areas based on the simulation result of a hydrologic and hydraulic model of catchment response due to several extreme rainfall events using HEC-HMS and HEC-RAS software. Rainfall and discharge data measured at the Ciberang-Sabagi water level gauge on 10 January 2013 were used to calibrate hydrological watershed parameters. The hydraulics channel routing is started from the planned location of the Sabo dam to the downstream control point. The next stage was the simulation of rainfall-runoff transformation and 1D unsteady flow channel routing for the 2, 5, and 10-years floods return periods. The main result of this study is a flood hazards map that shows the spatial distribution of the area and inundation depth for each return period of the flood.

scholarly journals ASSESSMENT OF VULNERABILITY IN AGRICULTURAL LAND IN FLOOD PRONE AREAS AND APPLICATION OF MOBILE SMART PHONE IN PROVIDING FLOOD HAZARD INFORMATION IN LAM RIVER BASIN (VIETNAM)

2021 ◽  
Vol 13 (2) ◽  
pp. 254-264
Author(s):  
Nguyen DUNG ◽  
◽  
Dang MINH ◽  
Bui AN ◽  
Nguyen NGA ◽  
...  
Keyword(s):  
River Basin ◽  
Agricultural Land ◽  
Flood Hazard ◽  
Smart Phone ◽  
Flood Management ◽  
Drainage Density ◽  
Slope Length ◽  
Risk Levels ◽  
High Hazard ◽  
Very High

Floods are considered to be one of the most costly natural hazards in the Lam river basin causing infrastructure damages as well as devastating the affected area and relatively high death toll. So prevention is necessary for shielding lives and properties. The flood management on the Lam River basin has been considering for many years to minimize damages caused by flooding. The flood hazard zoning map is one of the indispensable tools to provide information about hazard and risk levels in a particular area and to perform the necessary preventive and preparedness procedures. The multicriteria decision analysis based on geographic information systems is used to build a flood hazard map of the study area. The analytic hierarchy process is applied to extract the weights of six criteria affecting the areas where are prone to flooding hazards, including rainfall, slope, relative slope length, soil, land cover, and drainage density. The results showed in 91.32 % (20103.83 km2) of the basin located in the moderate hazard zones to very high hazard zones. Accordingly, this study also determined 4 vulnerability levels to agricultural land including low, medium, high, and very high. About 94% of the total area of agricultural land in the basin are classified into moderate to the very high hazard of flood vulnerability. The paper presents a method that allows flood risk areas in the Lam River basin to receive information about flood risks on a smartphone, making them more aware.

scholarly journals Flood Prediction Using Machine Learning Models: Literature Review

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1536 ◽  
Author(s):  
Amir Mosavi ◽  
Pinar Ozturk ◽  
Kwok-wing Chau
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Human Life ◽  
Cost Effective ◽  
Performance Comparison ◽  
Flood Prediction ◽  
Effective Strategies ◽  
Hybridization Data ◽  
Prediction Systems

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction of the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods. This survey can be used as a guideline for hydrologists as well as climate scientists in choosing the proper ML method according to the prediction task.

scholarly journals Numerical modelling of dynamic flood topographies in the Terai region, Nepal.

2020 ◽  
Author(s):  
Maggie J. Creed ◽  
Elizabeth H. Dingle ◽  
Hugh D. Sinclair ◽  
Dilip Gautam ◽  
Noel Gourmelen ◽  
...  
Keyword(s):  
Prediction Models ◽  
Flood Hazard ◽  
Field Measurements ◽  
Channel Switching ◽  
Gangetic Plain ◽  
Flood Prediction ◽  
Bed Elevation ◽  
Inundation Extent ◽  
Indo Gangetic Plain ◽  
Global Population

<p>Rivers sourced from the Himalayas support ~10% of the global population living on the Indo-Gangetic Plain. These rivers can be a source of devastating floods. Flood hazard maps used to inform early warnings systems in the Terai region in southern Nepal are based on static, outdated DEMs, which may not reflect the current river and floodplain topography. Sediment dynamics can change the river course and the distribution of flow down large bifurcation nodes, affecting flood inundation extent. These processes are rarely considered in flood prediction models for this region. In this study, using a 2D depth-averaged hydrodynamic model, several flood scenarios for the Karnali River are investigated, including different DEMs, variable bed elevations, and a scenario with bed levels modified at an important bifurcation node to reflect field observations. Inundation extent varied by upto 14% between scenarios for a 1-in-20 year flood discharge. Our results suggest that combining regular field measurements of bed elevation, with updated DEMs, could help to improve future flood prediction maps. Updating model input parameters is particularly important following large flood events and/or large landslides in the upstream catchment, which could increase bed aggradation and provoke channel switching in highly mobile, alluvial river systems.</p>

scholarly journals Flood Prediction Using Machine Learning, Literature Review

2018 ◽  
Author(s):  
Amir Mosavi ◽  
Pinar Ozturk ◽  
Chau Kwok-wing
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Human Life ◽  
Cost Effective ◽  
Performance Comparison ◽  
Flood Prediction ◽  
Hybridization Data ◽  
Mathematical Expressions ◽  
Prediction Systems

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

Spatio-temporal dynamics of flood regulating ecosystem services in the Arno river basin, Italy

2020 ◽  
Author(s):  
Stefano Mori ◽  
Tommaso Pacetti ◽  
Luigia Brandimarte ◽  
Enrica Caporali
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
River Basin ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Meteorological Data ◽  
Supply And Demand ◽  
Flood Management ◽  
Mitigation Measures ◽  
Retention Capacity

<p>Human activities can strongly influence the capacity of ecosystems to provide flood regulating ecosystem services (ES). Therefore, the effects of land use alteration, population migration and urbanization are key aspects to be considered when dealing with flood management. This study aims at analyzing the spatio‑temporal dynamics of flood regulating ES to support watershed management planning. The spatial explicit analysis of flood regulating ES is carried out with SWAT - Soil and Water Assessment Tool, using daily meteorological data between 2000 and 2014. Two indicators are elaborated in order to evaluate the retention capacity of each land use setting and to map the ES supply. Demand quantification is obtained from the information derived by the existing flood management plans (i.e. PAI-Piano per l’Assetto Idrogeologico and PGRA-Piano di Gestione del Rischio Alluvioni) which contain the identification and the perimeter of hydraulic hazard classes. Supply and demand data are then merged in order to obtain budget maps of flood regulating ES and their evolution from 1960 up to 2012 (1960, 1990, 2000 and 2012). The results show that both the demand and the supply of ecosystem services change during the time. With the increasing urbanization, the demand values have grown in the Arno floodplains, where residential, industrial and commercial zones are located. At the same time, land use changes (e.g. intensive agriculture) have caused negative effects on water regulation supply. This work shows the advantages of assessing flood regulating ES to improve flood regulation in the Arno river basin and provide a sound base of knowledge to identify floods prevention and mitigation measures.</p>

Flood risk management under climate change: a hydro-economic perspective

2018 ◽  
Vol 18 (5) ◽  
pp. 1832-1840 ◽  
Author(s):  
Rohini P. Devkota ◽  
Tek Maraseni
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Flood Hazard ◽  
Methodological Approach ◽  
Extreme Rainfall ◽  
Developed Countries ◽  
Flood Management ◽  
Policy Implications ◽  
Extreme Rainfall Events ◽  
Management Plans

Abstract Most developing countries, like Nepal, are expected to experience the greatest impact of climate change (CC) sooner and on a greater magnitude than other developed countries. Increase in the magnitude and frequency of extreme rainfall events is likely to increase the risk of flooding in rivers. The West Rapti River basin is one of the most flood prone and also one of the most dynamic and economically important basins of Nepal. This study elicits the willingness to pay (WTP) from the local people in the basin to reduce risks from possible floods due to CC. The WTP for flood mitigation in different flood hazard zones and flood scenarios were determined using referendum method and a face to face questionnaire survey. From a total of 720 households across all flood zones, a stratified randomly selected sample of 210 households was surveyed. The sample included households from a range of socio-economic backgrounds. The average WTP varied by flood hazard zone and within each zone, by CC-induced flood scenarios. The average WTP of respondents was highest for the critical flood prone zone, followed by moderate and low flood prone zones. Similarly, within each zone, the average WTP increased with increasing flood magnitudes due to CC. The variation of average WTP of respondents in different flood prone zones and scenarios indicate different levels of perceived severity. Moreover, the introduction of the concept of ‘man-day’ or ‘labour-day’ in WTP research is a novel and applicable methodological approach, particularly in the South Asian region. The findings of this study are useful for policy implications for the design of participatory flood management plans in the river basin.

scholarly journals Flood Hazard Mapping by Using Geographic Information System and Hydraulic Model: Mert River, Samsun, Turkey

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Vahdettin Demir ◽  
Ozgur Kisi
Keyword(s):  
River Basin ◽  
Flood Risk ◽  
Flood Hazard ◽  
Human Life ◽  
Hydraulic Model ◽  
Hazard Mapping ◽  
Digital Elevation ◽  
Risk Maps ◽  
Analysis System ◽  
Elevation Model

In this study, flood hazard maps were prepared for the Mert River Basin, Samsun, Turkey, by using GIS and Hydrologic Engineering Centers River Analysis System (HEC-RAS). In this river basin, human life losses and a significant amount of property damages were experienced in 2012 flood. The preparation of flood risk maps employed in the study includes the following steps: (1) digitization of topographical data and preparation of digital elevation model using ArcGIS, (2) simulation of flood lows of different return periods using a hydraulic model (HEC-RAS), and (3) preparation of flood risk maps by integrating the results of (1) and (2).

scholarly journals Flood Prediction Using Machine Learning, Literature Review

2018 ◽  
Author(s):  
Amir Mosavi ◽  
Pinar Ozturk ◽  
Kwok-wing Chau
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Comprehensive Evaluation ◽  
Human Life ◽  
Cost Effective ◽  
Performance Comparison ◽  
Flood Prediction ◽  
Hybridization Data ◽  
Mathematical Expressions ◽  
Prediction Systems

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

scholarly journals Flood Prediction and warning system using SVM and ELM models.

2019 ◽  
Vol 8 (4) ◽  
pp. 5366-5369
Keyword(s):  
Tamil Nadu ◽  
Prediction Models ◽  
Warning System ◽  
Flood Management ◽  
Support Vector ◽  
Flood Prediction ◽  
Live Feed ◽  
Svm Model ◽  
Learning Machine ◽  
Better Than

Seeing the rising amount of flood calamities worldwide flood management system are recently in limelight and are receiving the much needed attention. However the technologies used in determining and predicting the occurrence of a flood is somewhat inaccurate. Taking into consideration the number of lives at stake this project is aimed at introducing newer and possibly, more effective methods and techniques than its previously used flood prediction models. The proposed system seeks to implement machine learning by gathering the previously existing data along with a periodic live feed update so as to predict the chances of flood occurrence and so as to implement the necessary counteractive measures that can be deployed so as to evade such a mishap. The area taken into consideration for testing this new system is based on Chennai; capital of Tamil Nadu which spans over an area of 426 km2 .The study illustrates how a hybrid model is generated by taking all the data and using the Support Vector Machine (SVM) model and Extreme Learning Machine (ELM) model on it. The experimental results show that the integrated algorithm performs much better than other benchmarks. Moreover, testing the algorithm with live data makes it even more efficient and precise compared to other algorithms and proposed systems helping us to counteract real time fiascos. The main application of this system is to enable the user to warn and evacuate a mass population in case of a mishap.

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