scholarly journals FLOOD HAZARD MAPPING AT LONG XUYEN QUADRANGLE IN 2015 USING GEOGRAPHIC INFORMATION AND REMOTE SENSING

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 275-281
Author(s):  
N. T. H. Diep ◽  
T. H. Duy ◽  
P. K. Diem ◽  
N. T. B. Nam ◽  
N. T. T. Huong
Keyword(s):  
Flood Hazard ◽  
Mekong Delta ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Water Index ◽  
Flood Hazard Map ◽  
Severe Flood ◽  
Normalized Difference Water Index ◽  
Spss Software ◽  
Sentinel 2

<p><strong>Abstract.</strong> In recent year, the flooding has been occurred with higher frequency at Long Xuyen Quadrangle areas of Mekong Delta, Vietnam. It was considered as a major natural disaster which effects on the physical and spiritual in people’s life in this area. This research aims to generate a flood hazard map and assess the flood situation at Long Xuyen quadrangle in 2015. The MNDWI (Modification of Normalized Difference Water Index) extracting from Sentinel 2 image was used to map the flood extent at Long Xuyen quadrangle during rainy season in 2015. The statistics method was estimated correlation coefficient between flooding spatial distribution and hydrological stations on SPSS software. The results showed that the severe flood occurred from August to December in 2015. There were about 47.6% and 28.2% of the total area were inundated in October and August, respectively. The correlation between inundated areas and water level at Ha Tien and Chau Doc hydrological stations was 0.73 and 0.65 (p &amp;lt;0.01), respectively. The derived information was very essential and valuable for local managers in making decision on responding and mitigating to the flood disaster.</p>

scholarly journals Flood Hazard Assessment Using AHP in Corum, Turkey

2021 ◽  
Vol 12 (2) ◽  
pp. 01-26
Author(s):  
Derya Ozturk ◽  
◽  
Ilknur Yilmaz ◽  
Ufuk Kirbas ◽  
◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Flood Hazard ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Analytic Hierarchy ◽  
Flood Hazard Map ◽  
Structural Measures ◽  
High Flood ◽  
Hierarchy Process ◽  
Very High

In this study, the flood hazard of Corum province (Turkey) was investigated using the Analytic Hierarchy Process (AHP), which is one of the most popular Multi-criteria Decision Analysis (MCDA) methods, based on Geographic Information System (GIS). As a result of the AHP process, Corum province was categorized into five flood hazard classes: very high, high, medium, low, and very low. It was determined that 3% of the total area is under a very high flood hazard, and 25% is considered a high flood hazard. To assess the validity of the flood hazard map, the results were compared with the historical flood inventory. Our hazard map was compatible with the historical flood inventory, and our hazard map can now be used to estimate the areas that are threatened by possible floods. When the existing structural measures are overlapped with the hazard map in Corum, it is understood that a large part of the structural measures carried out to date have focused on the areas of very high and high flood hazard in the flood hazard map. Future structural measures and detailed studies should now address other areas identified as under threat in the flood hazard map. Our results suggest that the hazard assessment based on MCDA is suitable for flood hazard mapping.

scholarly journals Application of Fuzzy TOPSIS to Flood Hazard Mapping for Levee Failure

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 592 ◽  
Author(s):  
Tae Hyung Kim ◽  
Byunghyun Kim ◽  
Kun-Yeun Han
Keyword(s):  
Flood Hazard ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Dam Failure ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Hazard Maps ◽  
Levee Failure ◽  
Flood Hazard Map ◽  
Flood Hazard Maps

This paper proposes a new approach to consider the uncertainties for constructing flood hazard maps for levee failure. The flood depth, velocity, and arrival time were estimated by the 2-Dimensional model and were considered as flood indices for flood hazard mapping. Each flood index predicted from the 2-D flood analysis based on several scenarios was fuzzified to reflect the uncertainties of the indices. The fuzzified flood indices were integrated using the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), resulting in a single graded flood hazard map. This methodology was applied to the Gam river in South Korea and confirmed that the Fuzzy MCDM (Multiple Criteria Decision Making) technique can be used to produce flood hazard maps. The flood hazard map produced in this study compared with the current flood hazard map of MOLIT (Ministry of Land, Infrastructure and Transports). This study found that the proposed methodology was more advantageous than the current methods with regard to the accuracy and grading of the flood areas, as well as in regard to an integrated single map. This report is expected to be expand upon other floods, including dam failure and urban flooding.

scholarly journals Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets

2021 ◽  
Vol 13 (23) ◽  
pp. 4761
Author(s):  
Saeid Parsian ◽  
Meisam Amani ◽  
Armin Moghimi ◽  
Arsalan Ghorbanian ◽  
Sahel Mahdavi
Keyword(s):  
Remote Sensing ◽  
Analytical Hierarchy Process ◽  
Vegetation Index ◽  
Flood Hazard ◽  
Fuzzy Membership Function ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Topographic Wetness Index ◽  
Flood Hazard Map ◽  
Hierarchy Process

Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood hazard map with five classes of susceptible zones. The bi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) images, acquired before and on the day of the flood event, were used to evaluate the accuracy of the produced flood hazard map. The results indicated that 95.16% of the actual flooded areas were classified as very high and high flood hazard classes, demonstrating the high potential of this approach for flood hazard mapping.

Flash flood hazard mapping based on AHP with GIS and satellite information in Kampong Speu Province, Cambodia

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chhuonvuoch Koem ◽  
Sarintip Tantanee
Keyword(s):  
Land Use ◽  
Flash Flood ◽  
Flood Hazard ◽  
Geographical Information ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Content Type ◽  
Satellite Information ◽  
Flood Hazard Map ◽  
Hazard Levels

Purpose Cambodia is considered one of the countries that are most vulnerable to adverse effects of climate change, particularly floods and droughts. Kampong Speu Province is a frequent site of calamitous flash floods. Reliable sources of flash flood information and analysis are critical in efforts to minimize the impact of flooding. Unfortunately, Cambodia does not yet have a comprehensive program for flash flood hazard mapping, with many places such as Kampong Speu Province having no such information resources available. The purpose of this paper is, therefore, to determine flash flood hazard levels across all of Kampong Speu Province using analytical hierarchy process (AHP) and geographical information system (GIS) with satellite information. Design/methodology/approach The integrated AHP–GIS analysis in this study encompasses ten parameters in the assessment of flash flood hazard levels across the province: rainfall, geology, soil, elevation, slope, stream order, flow direction, distance from drainage, drainage density and land use. The study uses a 10 × 10 pairwise matrix in AHP to compare the relative importance of each parameter and find each parameter’s weight. Finally, a flash flood hazard map is developed displaying all areas of Kampong Speu Province classified into five levels, with Level 5 being the most hazardous. Findings This study reveals that high and very high flash flood hazard levels are identified in the northwest part of Kampong Speu Province, particularly in Aoral, Phnum Srouch and Thpong districts and along Prek Thnot River and streams. Originality/value The flash flood hazard map developed here provides a wealth of information that can be invaluable for implementing effective disaster mitigation, improving disaster preparedness and optimizing land use.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

Probabilistic Flood Hazard Maps at Ungauged Locations Using Multivariate Extreme Values Approach

2020 ◽  
Author(s):  
Kiran Kezhkepurath Gangadhara ◽  
Srinivas Venkata Vemavarapu
Keyword(s):  
Flood Risk ◽  
Flood Hazard ◽  
Flood Plain ◽  
Hydrologic Model ◽  
Regional Frequency Analysis ◽  
Hazard Map ◽  
Hazard Maps ◽  
Flood Hydrograph ◽  
Flood Hazard Map ◽  
Flood Hazard Maps

&lt;p&gt;Flood hazard maps are essential for development and assessment of flood risk management strategies. Conventionally, flood hazard assessment is based on deterministic approach which involves deriving inundation maps considering hydrologic and hydraulic models. A flood hydrograph corresponding to a specified return period is derived using a hydrologic model, which is then routed through flood plain of the study area to estimate water surface elevations and inundation extent with the aid of a hydraulic model. A more informative way of representing flood risk is through probabilistic hazard maps, which additionally provide information on the uncertainty associated with the extent of inundation. To arrive at a probabilistic flood hazard map, several flood hydrographs are generated, representing possible scenarios for flood events over a long period of time (e.g., 500 to 1000 years). Each of those hydrographs is routed through the flood plain and probability of inundation for all locations in the plain is estimated to derive the probabilistic flood hazard map. For gauged catchments, historical streamflow and/or rainfall data may be used to determine design flood hydrographs and the corresponding hazard maps using various strategies. In the case of ungauged catchments, however, there is a dearth of procedures for prediction of flood hazard maps. To address this, a novel multivariate regional frequency analysis (MRFA) approach is proposed. It involves (i) use of a newly proposed clustering methodology for regionalization of catchments, which accounts for uncertainty arising from ambiguity in choice of various potential clustering algorithms (which differ in underlying clustering strategies) and their initialization, (ii) fitting of a multivariate extremes model to information pooled from catchments in homogeneous region to generate synthetic flood hydrographs at ungauged target location(s), and (iii) routing of the hydrographs through the flood plain using LISFLOOD-FP model to derive probabilistic flood hazard map. The MRFA approach is designed to predict flood hydrograph related characteristics (peak flow, volume and duration of flood) at target locations in ungauged basins by considering watershed related characteristics as predictor/explanatory variables. An advantage of the proposed approach is its ability to account for uncertainty in catchment regionalization and dependency between all the flood hydrograph related characteristics reliably. Thus, the synthetic flood hydrographs generated in river basins appear more realistic depicting the observed dependence structure among flood hydrograph characteristics. The approach alleviates several uncertainties found in conventional methods (based on conceptual, probabilistic or geomorphological approaches) which affect estimation of flood hazard. Potential of the proposed approach is demonstrated through a case study on catchments in Mahanadi river basin of India, which extends over 141,600 km&lt;sup&gt;2&lt;/sup&gt; and is frequently prone to floods. Comparison is shown between flood hazard map obtained based on true at-site data and that derived based on the proposed MRFA approach by considering the respective sites to be pseudo-ungauged. Coefficient of correlation and root mean squared error considered for performance evaluation indicated that the proposed approach is promising.&lt;/p&gt;

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