Urban flood susceptibility assessment based on convolutional neural networks

Zhao, Gang; Pang, Bo; Xu, Zongxue; Peng, Danling; Zuo, Depeng

doi:10.1016/j.jhydrol.2020.125235

Cited by 102 publications

(59 citation statements)

References 56 publications

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“…In 2020, Wang et al proposed two CNN frameworks for flood susceptibility prediction [80]. Gang et al and Khosravi et al have applied CNN to the prediction of flood susceptibility in cities and Iran, respectively [66,67]. However, the application of CNN in flood susceptibility prediction is still rare [80].…”

Section: Assessment Of the Methodologymentioning

confidence: 99%

“…The CNN is still a neural network, and it carries the local connections among the different layers. In recent years, several CNN architectures have been established to solve increasingly complicated nonlinear problems, among which, the 1D-CNN is regarded as the most typical [67] and was used in this study. In general, the 1D-CNN has five neuronal layers, including an input layer, a convolutional layer, a pooling layer, a fully connected neural network layer, and an output layer.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

et al. 2021

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Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.

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Section: Assessment Of the Methodologymentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

et al. 2021

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“…Improved precision for flooded open areas (FO) (from 0.506 to 0.684) is obtained at the cost of a slight decrease in recall (from 0.842 to 0.824), while both precision and recall for flooded built-up areas (FB) increase. In another study by Zhao et al [47], CNN performed better than the SVM and random forests (RF), with an accuracy of 0.90 for CNN and 0.88 for LeNet-5 in the testing period. Similarly, Hosseiny et al [45] used RFs for flood detection, coupled with the MLP model for flood depth detection in the target regions.…”

Section: Literature Reviewmentioning

confidence: 98%

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

et al. 2021

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Floods have been a major cause of destruction, instigating fatalities and massive damage to the infrastructure and overall economy of the affected country. Flood-related devastation results in the loss of homes, buildings, and critical infrastructure, leaving no means of communication or travel for the people stuck in such disasters. Thus, it is essential to develop systems that can detect floods in a region to provide timely aid and relief to stranded people, save their livelihoods, homes, and buildings, and protect key city infrastructure. Flood prediction and warning systems have been implemented in developed countries, but the manufacturing cost of such systems is too high for developing countries. Remote sensing, satellite imagery, global positioning system, and geographical information systems are currently used for flood detection to assess the flood-related damages. These techniques use neural networks, machine learning, or deep learning methods. However, unmanned aerial vehicles (UAVs) coupled with convolution neural networks have not been explored in these contexts to instigate a swift disaster management response to minimize damage to infrastructure. Accordingly, this paper uses UAV-based aerial imagery as a flood detection method based on Convolutional Neural Network (CNN) to extract flood-related features from the images of the disaster zone. This method is effective in assessing the damage to local infrastructures in the disaster zones. The study area is based on a flood-prone region of the Indus River in Pakistan, where both pre-and post-disaster images are collected through UAVs. For the training phase, 2150 image patches are created by resizing and cropping the source images. These patches in the training dataset train the CNN model to detect and extract the regions where a flood-related change has occurred. The model is tested against both pre-and post-disaster images to validate it, which has positive flood detection results with an accuracy of 91%. Disaster management organizations can use this model to assess the damages to critical city infrastructure and other assets worldwide to instigate proper disaster responses and minimize the damages. This can help with the smart governance of the cities where all emergent disasters are addressed promptly.

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“…Recently, machine learning (ML) algorithms (e.g., random forest (RF), support vector machine (SVM), decision tree (DT), and artificial neural networks (ANN)) and optimized models proved their abilities to handle large numbers of variables and large datasets timely and accurately [1,4,21]. ML algorithms have been successfully applied in many applications such as landslide, flood, and wildfire susceptibility mapping [13,19,22,23]. However, the implementation and reliability of these methods still need further investigation in natural hazard prediction [15,19].…”

Section: Related Studiesmentioning

confidence: 99%

“…The higher value indicates higher accumulation and runoff flow and thus more sensitivity to the flood occurrence [3,7]. Moreover, distance to river and road (Figure 2f,g) are amongst the influential factors for the flood events [20,23,32], and road and river were collected from https://www.data. qld.gov.au/dataset/baseline-roads-and-tracks-queensland (accessed on 30 May 2021) and calculated using Euclidean distance analysis [36].…”

Section: The Topographic Factorsmentioning

confidence: 99%

Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

Kalantar

Ueda

Saeidi³

et al. 2021

Remote Sensing

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Large damages and losses resulting from floods are widely reported across the globe. Thus, the identification of the flood-prone zones on a flood susceptibility map is very essential. To do so, 13 conditioning factors influencing the flood occurrence in Brisbane river catchment in Australia (i.e., topographic, water-related, geological, and land use factors) were acquired for further processing and modeling. In this study, artificial neural networks (ANN), deep learning neural networks (DLNN), and optimized DLNN using particle swarm optimization (PSO) were exploited to predict and estimate the susceptible areas to the future floods. The significance of the conditioning factors analysis for the region highlighted that altitude, distance from river, STI, and slope played the most important roles, whereas SPI did not contribute to the hazardous situation. The performance of the models was evaluated against the statistical tests such as sensitivity, specificity, the area under curve (AUC), and true skill statistic (TSS). DLNN and PSO-DLNN models obtained the highest values of sensitivity (0.99) for the training stage to compare with ANN. Moreover, the validations of specificity and TSS for PSO-DLNN recorded the highest values of 0.98 and 0.90, respectively, compared with those obtained by ANN and DLNN. The best accuracies by AUC were evaluated in PSO-DLNN (0.99 in training and 0.98 in testing datasets), followed by DLNN and ANN. Therefore, the optimized PSO-DLNN proved its robustness to compare with other methods.

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Urban flood susceptibility assessment based on convolutional neural networks

Cited by 102 publications

References 56 publications

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets

UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection

Deep Neural Network Utilizing Remote Sensing Datasets for Flood Hazard Susceptibility Mapping in Brisbane, Australia

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