Flood Monitoring Research Articles

Design of Flood Barrier with Developed IoT-Based Flood Detection and Monitoring Systems

Due to Indonesia's tropical climate, it experiences frequent heavy rainfall, which is the primary cause of flooding. Despite various solutions implemented, the issue of flooding persists. Consumers demand an effective emergency flood protection solution to prevent water from entering homes at any time. This product is a flood barrier specifically designed for installation at the front door of houses. The development of this flood barrier product aims to address user needs and ensure flood resistance. To achieve this, the Kano Method and Quality Function Deployment (QFD) are utilized in the product design process, aligning it with customer desires. The Finite Element Analysis (FEA) is employed in testing to assess the flood barrier's ability to contain water effectively. Moreover, the data representing the user requirements is collected from residents in the Baleendah area. Those residents houses have been affected by flooding. This area faces significant economic losses due to flooding, estimated at IDR 5,490,292,000,000. Bandung Regency is projected to have the highest frequency of flooding incidents in West Java in 2022, contributing to the broader issue of flooding in the province and country. The developed flood barrier product has successfully met most customer needs, leading to customer satisfaction. The product offers a reasonable price, customer customization options, and user-friendly features. The integration of Internet of Things (IoT) capabilities has been well received by users. The flood barrier product also demonstrates its efficacy in retaining floodwater through its low equivalent elastic strain, equivalent stress, and deformation values against hydrostatic pressure. These attributes enable the product to withstand floodwaters and prevent their entry into households.

Analysis of the consequences of breakdowns of hydraulic structures and the occurrence of emergency situations

The problem of prevention and elimination of emergencies remains very urgent. The prediction of area flooding as a result of seasonal floods, or due to emergency situations is important and is solved on the basis of various technologies. Computer modeling methods based on direct hydrodynamic calculations of surface water dynamics seem to be an effective direction. Of particular interest is modeling the dynamics of filling of hydraulic structures in mountainous terrain. In this article, features of destruction of hydraulic structures in emergency situations on water bodies. The article also mentions the methods of monitoring water levels in the reservoirs. A description of flood monitoring technology is given, the results of its practical use in some regions are discussed, and directions for further development are outlined. This information is subsequently used to predict the emergency situation.

Towards Improved Flash Flood Forecasting over Dire Dawa, Ethiopia Using WRF-Hydro

Flash floods are increasingly frequent worldwide. Recent flooding in eastern Ethiopia resulted in casualties, destruction of property and interruptions of service. National flash flood forecasts made today primarily consider precipitation, putting less emphasis on surface processes. Enhancing accurate flash flood forecasts by accounting for surface processes and hydrological models together with a deeper understanding of heavy precipitation mechanisms is of paramount importance. To this end, an uncoupled WRF-Hydro model was calibrated for eastern Ethiopia to simulate extreme floods. Sensitivity analysis for August 2006 showed that infiltration runoff, hydraulic soil conductivity and saturated volumetric soil moisture with parameter values of 0.1, 1.5 and 1.0 produced realistic streamflow distribution. Extreme floods in March 2005 and April 2007 were further studied. The results showed that WRF-Hydro replicates temporal and spatial patterns well. Analysis using observational/reanalysis data revealed associated physical processes. Precipitation during these events exceeded long-term climatology and spanned wider areas in eastern Ethiopia. These heavy precipitation events are associated with strong upper-level westerly jet streams and rainfall-conducive circulation anomalies at lower levels. Positive outcomes from WRF-Hydro suggest operational implementation for flood monitoring and early warning systems in forecasting centers.

Flood Extent and Volume Estimation Using Remote Sensing Data

Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct a rapid and accurate assessment of the damage and take measures to restore critical infrastructure as quickly as possible. Remote sensing monitoring using artificial intelligence is a promising tool for estimating the extent of flooded areas. However, monitoring flood events still presents some challenges due to varying weather conditions and cloud cover that can limit the use of visible satellite data. Additionally, satellite observations may not always correspond to the flood peak, and it is essential to estimate both the extent and volume of the flood. To address these challenges, we propose a methodology that combines multispectral and radar data and utilizes a deep neural network pipeline to analyze the available remote sensing observations for different dates. This approach allows us to estimate the depth of the flood and calculate its volume. Our study uses Sentinel-1, Sentinel-2 data, and Digital Elevation Model (DEM) measurements to provide accurate and reliable flood monitoring results. To validate the developed approach, we consider a flood event occurred in 2021 in Ushmun. As a result, we succeeded to evaluate the volume of that flood event at 0.0087 km3. Overall, our proposed methodology offers a simple yet effective approach to monitoring flood events using satellite data and deep neural networks. It has the potential to improve the accuracy and speed of flood damage assessments, which can aid in the timely response and recovery efforts in affected regions.

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

Flood Monitoring and Alerting System

Abstract: Floods pose significant threats to communities worldwide, necessitating the development of efficient monitoring and alerting systems. This abstract presents an IoT-based Flood Monitoring and Alerting System that leverages the power of LoRaWAN (Long Range Wide Area Network) technology to provide real-time flood detection, monitoring, and timely alerts. The system consists of strategically placed water level sensors that use ultrasonic or pressure-based technology to accurately measure water levels. The sensor data is wirelessly transmitted to a central gateway through the LoRaWAN network, known for its long-range and low-power capabilities. A cloud-based platform processes and analyzes the collected data, identifying abnormal water level patterns and potential flood conditions. In case of a flood, the system triggers alerts via mobile notifications, email, and SMS to relevant authorities and stakeholders. Users can access real-time flood information, historical data, and visualizations through a user-friendly web or mobile interface. The system offers cost-effectiveness, extended range, low power consumption, early flood detection, timely response, data-driven decision-making, and has a brighter potential for future enhancements

Stakeholders’ composition, knowledge and use of earth observation data in wetland ecological assessments, Zimbabwe

The study analyses stakeholders’ knowledge and use of earth observation (EO) derived data in the assessment and monitoring of Driefontein and Intunjambili wetlands’ ecological conditions. Data was collected using a questionnaire survey administered to 282 randomly selected households and semi-structured interviews carried out with purposefully selected key informants representing institutional stakeholders. The results of this study show that the stakeholder categories identified in the two wetland areas are government departments, statutory bodies, non-governmental organisations, research institutions, academia and the local people. Most of the institutional stakeholders (55.6%) and household respondents (81.7%) do not have knowledge of EO data application in wetland conditions monitoring. Chi-Square test results further show that the households’ knowledge on use of remotely sensed derived products and services does not vary with age (p > 0.05), marital status (p > 0.05), level of education (p > 0.05), except gender in Driefontein (p < 0.05). However, the various stakeholder categories recommended wider application of earth observation products and services in the assessment of land use land cover changes, water quantity and quality, flood monitoring, fire outbreaks and the establishment of a wetland inventory to abate wetland degradation. The utilisation of EO data is constrained by lack of knowledge on the use of remotely sensed data in wetland ecological conditions assessment. Investment in capacity building initiatives for both technical and non-technical stakeholders involved in wetland management in Zimbabwe is required so as to enhance their skills in EO data utilisation.

URBAN ENVIRONMENTAL FLOOD SUSCEPTIBILITY USING A MULTI-LAYERED NEURAL NETWORK MODEL FROM SATELLITE IMAGERY SOURCES

&lt;p&gt;Recently, there has been a rise in research based on data-driven based machine learning models to address the issue of processing speed posed by various physiological simulations. The majority of studies, however, have concentrated on creating models for particular water sources, drainage systems, and gauge stations area. Therefore, unless further data is supplied and the models are subsequently trained, their outcomes cannot be effectively applied to different scenarios. With its excellent spatio-temporal accuracy range with the advanced analytics, the forthcoming Global Navigations Satellite Systems (GNSSs) technology offers a novel method for the monitoring of flood disasters in dynamic manner. This study recommends a Multi-layered Neural Network (MNN) for flood monitoring, which combines a Convolutional Neural Networking (CNN) along with a backward propagation based (BP) learning network. The CNN component is used to obtain the abstracted features from full delay doppler maping images. The BP section is provided with GNSSs characteristics, such as reflection of surface with power range, and also the vegetation attributes from the Soil Moistening Active Passives (SMAP) satellite source data. In comparison to physiologically based models, the results shown that the CNN-oriented system makes accurate predictions on "unseen" areas of catchment with a substantially shorter processing time. The obtained results imply that, with respect to accuracy of prediction, the patch-enabled option is superior to the resizing-based option. Additionally, every experiment has demonstrated that the forecast of flow speed is more precise than the prognosis of water depth, indicating that the water deposition is more complex to information about global level elevations than the flow speed with velocity.&lt;/p&gt;&#x0D;

Flood Monitoring and Early Warning Systems – An IoT Based Perspective

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.

Design and implementation of sustainable solar energy harvesting for low-cost remote sensors equipped with real-time monitoring systems

Data acquisition systems, such as Wireless Smart Sensor Networks (WSSNs) can increase the resilience of infrastructure by providing real-time monitoring and data collection of environmental parameters. Yet, sustainable energy supplies for sensor networks established in remote and inaccessible areas still present a challenge. Previously, researchers have attempted to address this difficulty by proposing different energy systems including solar energy harvesting, however, significant prolonged experimental data for the operation of extensive networks powered by solar energy has not been reported. This paper presents an original design and implementation of an energy system for a large WSSN and provides the sensors' power status data over a significant duration. A network of low-cost flood monitoring sensors, including twenty-six water level sensors, twenty rain gauges, and eight communication nodes were deployed and tested on summer and fall 2022 at six remote locations at the northern New Mexico Pueblo, Ohkay Owingeh. A thermometer and a humidity sensor were added to each communication node to record temperature and air's moisture level. In addition, a networked voltage monitoring system was deployed to observe the sensors energy status in real-time. The items of the WSSN are composed of two differing energy circuits suited for their energy demands. The sensors' energy circuits contain a photovoltaic panel, a lithium-polymer battery, a control device, and a DC-to-DC converter. Whereas the communication nodes contain another photovoltaic panel, a lead-acid battery, and a solar charging controller. The findings provide a perspective on the long-term field deployment of WSSNs consisting of low-cost sensors.

Application of traditional manual measurements and remote sensing technologies in monitoring natural disasters

Natural disasters have occurred frequently worldwide. Some manual measurements and remote sensing technologies are widely believed to be effective and convenient in hazard monitoring. This study illustrates the causes and impacts of three natural disasters including floods, wildfires and earthquakes, and analyzes the application of traditional and remote sensing technologies in monitoring these natural disasters. In flood monitoring, traditional tools include meteorological and hydrological detection facilities and equipment could obtain rainfall and water level. Remote sensing technologies could extract water bodies and make disaster evaluation. In wildfire monitoring, traditional tools include the standard equipment of urban fire departments, portable pumps, tank trucks, and earth-moving equipment. Remote sensing technologies could be applied to fuel mapping and fire estimates. In earthquake monitoring, traditional tools include seismic measurement, crustal deformation observation, geomagnetic measurement, telluric observation, gravity observation, in-situ stress observation, and groundwater dynamic observation. Remote sensing technologies could help monitor through house images and thermal data. Remote sensing technologies bring natural disaster monitoring systems a great revolution, both in data acquisition and processing methods.

Statistical downscaling of GRACE TWSA estimates to a 1-km spatial resolution for a local-scale surveillance of flooding potential

The Gravity Recovery and Climate Experiment (GRACE) paved the way for large-scale monitoring of the hydrological extremes. However, local scale analysis is aslo challenging due to the coarse resolution of the GRACE estimates. The feasibility of the downscaled GRACE data for the flood monitoring in the Kizilirmak Basin (KB) in Türkiye is investigated in this study by integrating the GRACE and hydrological model outputs of a random forest approach. Results suggest that the TWSA, over the Asagi Kizilirmak Basin (AKB), is ascending with an annual rate of +3.51mm/yr; while the Orta Kizilirmak Basin (OKB), Yukari Kizilirmak Basin (YKB), Delice Basin (DB), Develi Kapali Basin (DKB), and Seyfe Kapali Basin (SKB) showed descending trend respectively as -1.15mm/yr, -1.58mm/yr, -1.14mm/yr, -2.34mm/yr, and -1.31mm/yr. The hydrological status of the basin showed that in 2003, 2005, 2010–2013, and 2015–2016 periods the study area was prone to the inundation. Hence, by validating the Flood Potential Index (FPI) rates acquired from the downscaled GRACE data, it was shown that the best correlation coefficient (0.73) between FPI and streamflow (Q) is associated with the SKB. It is also concluded that the downscaled TWSA associated with the fine-resolution models depicts acceptable accuracy in determination of the flood potential at local scales.

Iot Based Flood Detection, Alarm and Monitoring System Using Multilayer Perceptron and Regression

Flooding is a common natural event caused by heavy rainfall and high tides in the Philippines. While this disaster cannot be prevented, people can prepare themselves to face it. The City of Ilagan in the province of Isabela is one of the highly prone areas to flooding caused by the welling of the Cagayan River. Many communities are living in low-lying areas which most likely experiencing floods. An analysis of the location is conducted including the people living in the area. The analysis resulted in the development of an IoT-based technology for detection and early warning signals to people using sms notifications that can help lessen the difficulty of evacuation. The system uses Arduino UNO as a microcontroller where sensors are attached. These sensors are Light Detection and Ranging (LiDAR) for flood level measurement in feet (ft), Rain Gauge to measure the precipitation rate (mm/hr), and Flow Rate Meter to measure the fluctuation flow of the river in (L/hr). Data gathered from these sensors are processed and sent immediately to people living nearby to monitor the flood level in real-time. The predictive models are developed using the Pinacanauan River dataset taken from the river stations. The multilayer perceptron is used to develop the predictive model with 99% accuracy. The data from sensors are used also and processed using linear regression and calculated as 88% accurate and significant for prediction.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction: GF-FloodNet

ABSTRACT Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack of available labeled data in actual needs. In this paper, we propose a high-resolution multi-source remote sensing dataset for flood area extraction: GF-FloodNet. GF-FloodNet contains 13388 samples from Gaofen-3 (GF-3) and Gaofen-2 (GF-2) images. We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it. Compare with other flood-related datasets, GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels, but also consists of multi-source remote sensing data. We thoroughly validate and evaluate the dataset using several deep learning models, including quantitative analysis, qualitative analysis, and validation on large-scale remote sensing data in real scenes. Experimental results reveal that GF-FloodNet has significant advantages by multi-source data. It can support different deep learning models for training to extract flood areas. There should be a potential optimal boundary for model training in any deep learning dataset. The boundary seems close to 4824 samples in GF-FloodNet. We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

Real-time social media sentiment analysis for rapid impact assessment of floods

Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions.Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time.In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time.

Flood Susceptibility Mapping Using Logistic Regression Analysis In Lam Khan Chu Watershed, Chaiyaphum Province, Thailand

Due to Tropical Storm Dianmu’s influence in the Lam Khan Chu watershed (LKCW) area, central Thailand saw its worst flood in 50 years from September 23 to September 28, 2021. The flooding lasted for 1-2 months. The objective of this research is to study flood susceptibility using logistic regression analysis in LCKW area. According to the study 11 floods occurred repeatedly between 2005 and 2021, in the southern of Bamnetnarong district and continued northeast to Chaturat district and Bueng Lahan swamp. These areas are the main waterways of the LKCW area, the Lam Khan Chu stream and the Huai Khlong Phai Ngam, for which the dominant flow patterns are braided streams. The main factors influencing flooding are geology, stream frequency, topographic wetness index, drainage density, soil, stream power index, land-use, elevation, mean annual precipitation, aspect, distance to road, distance to village, and distance to stream. The results of the logistic regression analysis shed light on these factors. All such variables were demonstrated by the β value coefficient. The area’s susceptibility to flooding was projected on a map, and it was discovered to have extremely high and high levels of susceptibility, encompassing regions up to 148.308 km2 (8.566%) and 247.421 km2 (14.291%), respectively, in the vicinity of the two main river sides of the watershed. As a result of this research the flood susceptibility map will be used as a guideline for future flood planning and monitoring.

Decoding the Vitality of Earth Observation for Flood Monitoring in the Lower Godavari River Basin, India

Decoding the Vitality of Earth Observation for Flood Monitoring in the Lower Godavari River Basin, India

Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin

The terrestrial water storage anomaly (TWSA) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO) provides a new means for monitoring floods. However, due to the coarse temporal resolution of GRACE/GRACE-FO, the understanding of flood occurrence mechanisms and the monitoring of short-term floods are limited. This study utilizes a statistical model to reconstruct daily TWS by combining monthly GRACE observations with daily temperature and precipitation data. The reconstructed daily TWSA is utilized to monitor the catastrophic flood event that occurred in the middle and lower reaches of the Yangtze River basin in 2020. Furthermore, the study compares the reconstructed daily TWSA with the vertical displacements of eight Global Navigation Satellite System (GNSS) stations at grid scale. A modified wetness index (MWI) and a normalized daily flood potential index (NDFPI) are introduced and compared with in situ daily streamflow to assess their potential for flood monitoring and early warning. The results show that terrestrial water storage (TWS) in the study area increases from early June, reaching a peak on 19 July, and then receding till September. The reconstructed TWSA better captures the changes in water storage on a daily scale compared to monthly GRACE data. The MWI and NDFPI based on the reconstructed daily TWSA both exceed the 90th percentile 7 days earlier than the in situ streamflow, demonstrating their potential for daily flood monitoring. Collectively, these findings suggest that the reconstructed TWSA can serve as an effective tool for flood monitoring and early warning.

Characterizing the 2020 summer floods in South China and effects on croplands

Floods occur more frequently in the context of climate change; however, flood monitoring capacity has not been well established. Here, we used a synergic mapping framework to characterize summer floods in the middle and lower reaches of the Yangtze River Plain and the effects on croplands in 2020, from both floodextent and intensity perspectives. We found that the total flood extent was 4936km2 from July to August, and for flood intensity, 1658, 1382, and 1896km2 of areas experienced triple, double, and single floods. A total of 2282km2 croplands (46% of the flooded area) were inundated mainly from Poyang and Dongting Lake Basins, containing a high ratio of moderate damage croplands (47%). The newly increased flooding extent in 2020 was 29% larger than the maximum ever-flooded extent in 2015-2019. This study is expected to provide a reference for rapid regional flood disaster assessment and serving mitigation.

Stereoscopic Monitoring Methods for Flood Disasters Based on ICESat-2 and Sentinel-2 Data

Climate change has led to an increased frequency of extreme precipitation events, resulting in increased damage from rainstorms and floods. Rapid and efficient flood forecasting is crucial. However, traditional hydrological simulation methods that rely on site distribution are limited by the limited availability of data and cannot provide fast and accurate flood monitoring information. Therefore, this study took the flood event in Huoqiu County in 2020 as an example and proposes a three-dimensional flood monitoring method based on active and passive satellites, which provides effective information support for disaster prevention and mitigation. The experimental results indicated the following: (1) the flood-inundated area was 704.1 km2, with the Jiangtang Lake section of the Huaihe River and the southern part of Chengdong Lake being the largest affected areas; (2) water levels in the study area ranged from 15.36 m to 17.11 m, which is 4–6 m higher than the original water level. The highest flood water level areas were the Jiangtang Lake section and the flat area in the south of Chengdong Lake, with Chengdong Lake and the north of Chengxi Lake having the greatest water level increase; (3) the flood water depth was primarily between 4 m and 7 m, with a total flood storage capacity of 2833.47 million m3, with Jiangtang Lake having the largest flood storage capacity; and (4) the rainstorm and flood disaster caused a direct economic loss of approximately CNY 7.5 billion and affected a population of approximately 91 thousand people. Three-dimensional monitoring of floods comprehensively reflects the inundation status of floods and can provide valuable information for flood prediction and management.