Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry

Zazo, Santiago; Rodríguez‐Gonzálvez, Pablo; Molina, José-Luis; González‐Aguilera, Diego; Agudelo-Ruiz, Carlos Andrés; Hernández-López, David

doi:10.3390/rs10101566

Cited by 23 publications

(19 citation statements)

References 78 publications

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“…Every year, flood incidents are accountable for huge impacts on social well being and economic infrastructure all over the world. Mapping flooding extent during a flood event has become a key tool needed to assist various private and government disaster management departments (local and state government emergency departments, and environmental groups) in mitigating, responding to and recovering from flood disasters [1][2][3]. Although these organizations seek different types of quantitative and qualitative information, their primary requirement is rapid acquisition of maps showing the extent of flood affected areas to plan relief work efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…However, with advancements in airborne technology, it is possible to conduct an aerial survey of extensive flooded areas for ground truth collection as we have observed in the studies by Damian Ortega-Terol et al [5] where the authors proposed a low-cost aircraft-based survey that can help in classification for detection of large woody debris along a segment of Jucar River in Spain. In [3], the authors proposed a low-cost aerial photogrammetry method by combining a reduced cost passive sensors on board an ultra-light aerial platform to obtain precise topographic data from digital aerial images for flood hazard assessment in the upper basin of Adaja River in Spain. The aerial observation is, however, difficult for a flood event over a large geographical area since it requires enough time and resources [4,6].…”

Section: Introductionmentioning

confidence: 99%

“…The first approach is developing hydraulic or hydrologic models for mapping flooded areas, and the second approach is applying classification algorithms for mapping flooded areas. Flood inundation modelling is a complex hydrodynamic process that may contain large uncertainties [3,8]; for this reason, many researchers in their studies have proposed methods to improve over the flooding inundation modelling, as for example, in [3], where the authors proposed an integrated methods of geomatics and fluvial hydraulics to improve the information about flood behaviour.…”

Section: Introductionmentioning

confidence: 99%

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Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

et al. 2019

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Remote sensing technology in recent years has been regarded the most important source to provide substantial information for delineating the flooding extent to the disaster management authority. There have been numerous studies proposing mathematical or statistical classification models for flood mapping. However, conventional pixel-wise classifications methods rely on the exact match of the spectral signature to label the target pixel. In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. We utilised the spatial information from the neighbouring area of target pixel in classification. A total of 64 different models were generated and trained with a variable neighbourhood size of training samples and number of learnable filters. The training results revealed that the model trained with 3 × 3 neighbourhood sized training samples and with 32 convolutional filters achieved the best performance out of the experiments. A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. A comparison of our proposed classification model to the conventional support vector machines (SVM) classification model shows that the F-CNNs model was able to detect flooded areas more efficiently than the SVM classification model. For example, the F-CNNs model achieved a maximum precision rate (true positives) of 76.7% compared to 45.27% for SVM classification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

et al. 2019

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“…There are many complex uncertainties, which lead to the risk in reservoir flood control operation, such as data accuracy, hydrological and hydraulic models, human factors and so on [32,33]. This study mainly considers the uncertainties of flood forecast, water level-storage and discharge capacity curves, which have been widely concerned and are considered to have a greater impact on flood control operation [8,9,14,34].…”

Section: Stochastic Simulation Of Uncertaintiesmentioning

confidence: 99%

Risk Assessment and Decision-Making Based on Mean-CVaR-Entropy for Flood Control Operation of Large Scale Reservoirs

Wang

Zhou

Huang

et al. 2019

Water

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The risk inevitably exists in the process of flood control operation and decision-making of reservoir group, due to the hydrologic and hydraulic uncertain factors. In this study different stochastic simulation methods were applied to simulate these uncertainties in multi-reservoir flood control operation, and the risk caused by different uncertainties was evaluated from the mean value, extreme value and discrete degree of reservoir occupied storage capacity under uncertain conditions. In order to solve the conflict between risk assessment indexes and evaluate the comprehensive risk of different reservoirs in flood control operation schemes, the subjective weight and objective weight were used to construct the comprehensive risk assessment index, and the improved Mahalanobis distance TOPSIS method was used to select the optimal flood control operation scheme. The proposed method was applied to the flood control operation system in the mainstream and its tributaries of upper reaches of the Yangtze River basin, and 14 cascade reservoirs were selected as a case study. The results indicate that proposed method can evaluate the risk of multi-reservoir flood control operation from all perspectives and provide a new method for multi-criteria decision-making of reservoir flood control operation, and it breaks the limitation of the traditional risk analysis method which only evaluated by risk rate and cannot evaluate the risk of the multi-reservoir flood control operation system.

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“…Recently, the integration of remote sensing (RS) and GIS have been used to map flooded areas [39,[41][42][43][44][45]. It is suitable to perform flood detection using remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China

et al. 2018

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Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of this work is to investigate the use a novel hybrid technique by integrating multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM), which is constructed by ensemble of decision making trial and evaluation laboratory (DEMATEL), analytic network process, weighted linear combinations (WLC) and interval rough numbers (IRN) techniques in the case study at Shangyou County, China. Specifically, we improve the DEMATEL method by applying IRN to determine connections in the network structure based on criteria and to accept imprecisions during collective decision making. The application of IRN can eliminate the necessity of additional information to define uncertain number intervals. Therefore, the quality of the existing data during collective decision making and experts’ perceptions that are expressed through an aggregation matrix can be retained. In this work, eleven conditioning factors associated with flooding were considered and historical flood locations were randomly divided into the training (70% of the total) and validation (30%) sets. The flood susceptibility map validates a satisfactory consistency between the flood-susceptible areas and the spatial distribution of the previous flood events. The accuracy of the map was evaluated by using objective measures of receiver operating characteristic (ROC) curve and area under the curve (AUC). The AUC values of the proposed method coupling with the WLC fuzzy technique for aggregation and flood susceptibility index are 0.988 and 0.964, respectively, which proves that the WLC fuzzy method is more effective for FSM in the study area. The proposed method can be helpful in predicting accurate flood occurrence locations with similar geographic environments and can be effectively used for flood management and prevention.

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Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry

Cited by 23 publications

References 78 publications

Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information

Risk Assessment and Decision-Making Based on Mean-CVaR-Entropy for Flood Control Operation of Large Scale Reservoirs

A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China

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