Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies

Xue, Fan; Gao, Wei; Chao, Yin; Chen, Xinyu; Xia, Zhihong; Lv, Yunzhe; Zhou, Yangyang; Wang, Mengmeng

doi:10.1109/jstars.2022.3176388

Cited by 10 publications

(5 citation statements)

References 51 publications

(54 reference statements)

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“…Tariq et al [13] contributed to the field with a study on flash flood susceptibility assessment and zonation, integrating the analytic hierarchy process and frequency ratio model with diverse spatial data. Xue et al [14] proposed flood monitoring through the integration of the flood indexes and probability distributions of water bodies. Phy et al [15] provided a comprehensive review of flood-hazard and -management activities in Cambodia, identifying knowledge gaps and suggesting research directions.…”

Section: State Of the Artmentioning

confidence: 99%

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Di Stefano,

Chiappini,

Sanità

et al. 2024

GaEE

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Hydrogeological risks that are associated with rivers have emerged as a significant concern worldwide, impacting both natural ecosystems and human settlements. This contribution presents an interdisciplinary project that leverages many technologies for data-acquisition and modeling to comprehensively analyze and manage risks in riverine environments. The project integrates geomatics, geological, and hydrological techniques to provide a holistic understanding of river dynamics and their associated hazards. As a central component of this project, geomatics plays a pivotal role in instrumental field surveying through the deployment of photogrammetry and LiDAR instruments. Remote-sensing data from satellite imagery further enriches the project’s temporal analysis capabilities. By analyzing this data over time, researchers can monitor changes in river patterns, land use, and climate-related variables; this helps identify trends and potential triggers for hydrological events. To manage and integrate the vast amount of geospatial information that is generated, a geodatabase within a geographic information system (GIS) has been established. It enables efficient data storage, retrieval, and analysis, fostering collaboration among multidisciplinary researcher teams. This system offers tools for risk-assessment, modeling, and scenario planning; these allow for proactive measures for mitigating hydrological risks.

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Section: State Of the Artmentioning

confidence: 99%

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Di Stefano,

Chiappini,

Sanità

et al. 2024

GaEE

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“…The covariance between the features and the class, cov(x, y), is computed using formula (3), where E(xy) represents the expected value of the product of x and y, and E(x) and E(y) denote the expected values of x and y, respectively.…”

Section: Data Dimension Reductionmentioning

confidence: 99%

“…Flooding has been a devastating aspect of human civilization for many centuries, as it affects the lives and livelihoods of millions [1,2]. Therefore, effective flood early warning systems are essential to reduce the losses from floods [3,4]. Monitoring floods enable authorities to make better decisions to reduce the impact of floods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Thankappan,

Mary,

Yoon

et al. 2023

CMC

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Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in IoT networks. The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency. Real-world flood data is used for validation, demonstrating the superior performance of the AM-BP algorithm compared to traditional methods. In addition, multilayer high-end computing architecture (MLCA) is used to handle weather data such as rainfall, river water level, soil moisture, etc. The AM-BP's real-time abilities enable proactive flood management, facilitating timely responses and effective disaster mitigation. Furthermore, the AM-BP algorithm can analyze large and complex datasets, integrating environmental and climatic factors for more accurate flood prediction. The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%, 96.4% F1-Measure, 97% Precision, and 95.9% Recall. The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks, contributing to more resilient and efficient flood control strategies, and ensuring the safety and well-being of communities at risk of flooding.

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“…Xue et al proposed the Sentinel image's normalized difference flood index (NDFI) with the summer permanent water bodies (SPWB) based NDFI-SPWB framework [33]. This framework aims to interpret the flood maps visually and decide the misclassification and omissions.…”

Section: Flood Monitoring Using Sentinel Satellite Imagesmentioning

confidence: 99%

Flood Monitoring and Early Warning Systems – An IoT Based Perspective

Ahmed,

Mohammed Siddique,

Mohammad Shahid Husain

2023

EAI Endorsed Trans IoT

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One of the most frequently occurring calamities around the world is the flood. For flood prone areas or countries, an essential part of their governance is flood management. The necessity to continuously review and analyse the adverse or ambient environmental conditions in real-time demands developing a monitoring system so that floods could be detected beforehand. This paper discusses different Internet of Things (IoT) based techniques and applications implemented for efficient flood monitoring and an early warning system and it is observed that in future, the combination of IoT and Synthetic Aperture Radar (SAR) data may be helpful to develop robust and secure flood monitoring and early warning system that provides effective and efficient mapping during natural disasters. The emerging technology in the discipline of computing is IoT, an embedded system that enables devices to gather real-time data to further store it in the computational devices using Wireless Sensor Networks (WSN) for further processing. The IoT based projects that can help collect data from sensors are an added advantage for researchers to explore in providing better services to people. These systems can be integrated with cloud computing and analyzing platforms. Researchers recently have focussed on mathematical modeling based flood prediction schemes rather than physical parametric based flood prediction. The new methodologies explore the algorithmic approaches. There have been many systems proposed based on analog technology to web-based and now using mobile applications. Further, alert systems have been designed using web-based applications that gather processed data by Arduino Uno Microcontroller which is received from ultrasonic and rain sensors. Additionally, the machine learning based embedded systems can measure different atmospheric conditions such as temperature, moisture, and rains to forecast floods by analyzing varying trends in climatic changes.

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Flood Monitoring by Integrating Normalized Difference Flood Index and Probability Distribution of Water Bodies

Cited by 10 publications

References 51 publications

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Geomatics-enabled Interdisciplinary Approach Based on Geospatial Data Processing for Hydrogeological Risk-analysis

Adaptive Momentum-Backpropagation Algorithm for Flood Prediction and Management in the Internet of Things

Flood Monitoring and Early Warning Systems – An IoT Based Perspective

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