Aerial Platform Reliability for Flood Monitoring Under Various Weather Conditions: A Review

Zakaria, Shazrizil; Mahadi, Muhammad Razif; Abdullah, Ahmad Fikri; Abdan, Khalina

doi:10.5194/isprs-archives-xlii-3-w4-591-2018

Cited by 8 publications

(4 citation statements)

References 108 publications

(78 reference statements)

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“…After detailed content analysis and examination of the methods adopted in each study for flood forecasting, we further categorized these studies based on the processing method used by the authors. For example, the use of machine learning (ML) methods for flood prediction has been commonly found in the literature [44][45][46][47]. This includes the use of CNN, AI, KNN, Bayesian linear, SVM, etc.…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing Methods for Flood Prediction: A Review

Munawar

Hammad

Waller

2022

Sensors

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Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely; therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.

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Section: Discussionmentioning

confidence: 99%

Remote Sensing Methods for Flood Prediction: A Review

Munawar

Hammad

Waller

2022

Sensors

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“…Furthermore, river management and fluvial networks have received considerable attention for UAV applications [11][12][13][14][15]. Additionally, flood monitoring and assessment are one of the major fields in which UAV aerial surveying data have been used [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Lee,

Hwang,

Singh

2024

Remote Sensing

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Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from the motion technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps and lined shapes. Therefore, a nonparametric estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with synthetically simulated trapezoidal, U-shape and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps as well as different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.

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“…Accordingly, Unmanned Aerial Vehicles (UAVs) can be used as efficient tools that can capture high-resolution spatial images from the target sites [22]. These UAVs are widely used these days instead of the traditional imaging tools such as satellite imaging and GPSbased monitoring as smart technologies in the industry 4.0 era [37][38][39][40][41]. UAVs can quickly collect precise image data and transmit it to their respective off-site servers for sharper, smarter, and more informed responses [41].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

et al. 2021

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Floods are one of the most fatal and devastating disasters, instigating an immense loss of human lives and damage to property, infrastructure, and agricultural lands. To cater to this, there is a need to develop and implement real-time flood management systems that could instantly detect flooded regions to initiate relief activities as early as possible. Current imaging systems, relying on satellites, have demonstrated low accuracy and delayed response, making them unreliable and impractical to be used in emergency responses to natural disasters such as flooding. This research employs Unmanned Aerial Vehicles (UAVs) to develop an automated imaging system that can identify inundated areas from aerial images. The Haar cascade classifier was explored in the case study to detect landmarks such as roads and buildings from the aerial images captured by UAVs and identify flooded areas. The extracted landmarks are added to the training dataset that is used to train a deep learning algorithm. Experimental results show that buildings and roads can be detected from the images with 91% and 94% accuracy, respectively. The overall accuracy of 91% is recorded in classifying flooded and non-flooded regions from the input case study images. The system has shown promising results on test images belonging to both pre- and post-flood classes. The flood relief and rescue workers can quickly locate flooded regions and rescue stranded people using this system. Such real-time flood inundation systems will help transform the disaster management systems in line with modern smart cities initiatives.

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Aerial Platform Reliability for Flood Monitoring Under Various Weather Conditions: A Review

Cited by 8 publications

References 108 publications

Remote Sensing Methods for Flood Prediction: A Review

Remote Sensing Methods for Flood Prediction: A Review

KNN Local Linear Regression for Demarcating River Cross-Sections with Point Cloud Data from UAV Photogrammetry URiver-X

Application of Deep Learning on UAV-Based Aerial Images for Flood Detection

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