Flood Early Warning System Research Articles

Economic Value of Flood Forecasts and Early Warning Systems: A Review

Economic Value of Flood Forecasts and Early Warning Systems: A Review

Enhancing Flood Management and Early Warning Systems in Mountain Rivers: An ANN-Based Approach

The study focuses on developing an HEC-RAS 1D model to simulate flood hydrographs at desired locations, considering the inflows from two different rivers Rishiganga and Dhauliganga. The objective is to analyze the impact of inflow hydrographs from the aforementioned rivers, utilizing three sets of flood data: high flow (2400 m3/sec), low flow (15 m3/sec), and 1.5 times the high flow (3600 m3/sec) peaks. An Artificial Neural Network (ANN) model, employing the Feed-forward Backpropagation method, is utilized to train the neurons using the maximum flow hydrograph. The trained ANN model is subsequently tested with two unseen inflow hydrographs of low and 1.5 x high flow to evaluate its robustness. The findings reveal that the ANN model performs very well in flood estimation, providing efficient predictions based on the testing with the maximum flow and validating with the minimum and 1.5 times higher inflow hydrographs. Its primary advantage lies in saving time, enabling timely actions when flood warnings are issued. This research significantly contributes to flood management and shall enhance the lead time in disseminating early warning to local authorities and communities. Work shall help in providing timely and accurate flood predictions for proactive measures during any flood event. The study’s significance is rooted in its potential to enhance flood preparedness and response in areas affected by the Rishiganga and Dhauliganga rivers, ultimately ensuring the safety and well-being of local communities and infrastructures.

Early warning system for floods at estuarine areas: combining artificial intelligence with process-based models

AbstractFloods are among the most common natural disasters, causing countless losses every year worldwide and demanding urgent measures to mitigate their impacts. This study proposes a novel combination of artificial intelligence and process-based models to construct a flood early warning system (FEWS) for estuarine regions. Using streamflow and rainfall data, a deep learning model with long short-term memory layers was used to forecast the river discharge at the fluvial boundary of an estuary. Afterwards, a hydrodynamic process-based model was used to simulate water levels in the estuary. The river discharge predictors were trained using different forecasting windows varying from 3 h to 36 h to assess the relationship between the time window and accuracy. The insertion of attention layers into the network architecture was evaluated to enhance forecasting capacity. The FEWS was implemented in the Douro River Estuary, a densely urbanised flood-prone area in northern Portugal. The results demonstrated that the Douro Estuary FEWS is reliable for discharges up to 5000 m3/s, with predictions made 36 h in advance. For values higher than this, the uncertainties in the model predictions increased; however, they were still capable of detecting flood occurrences.

Modeling the dynamics of social preparedness with regional interactions to explore efficient flood early warning systems

Abstract Flood early warning systems (FEWS) are essential in mitigating flood damage. To optimize their effectiveness, it is important to understand how people respond to warnings and prepare for flooding events. The key factors influencing social preparedness include (1) direct and (2) indirect experiences of floods and (3) trust in warnings. However, existing socio-hydrological models do not incorporate all these elements. To include these elements for social preparedness, we propose an idealized model that allows multiple regions to influence one another (i.e., regional interactions). We investigate the dynamics of social preparedness in a society composed of regions with varying infrastructure levels (e.g., levee heights) and explore strategies for developing a socially efficient FEWS. Numerical analyses reveal that in a society that has a region characterized by a low infrastructure level (i.e., a region with frequent floods), regional interactions lead to a pronounced cry wolf effect due to false alarms from other regions, diminishing social preparedness in the low-infrastructure region. These interactions also prevent a warning strategy that optimizes the natural science-based index (i.e., threat score) from maximizing social efficiency. Conversely, in a society that has a region characterized by a high infrastructure level (i.e., a region with infrequent floods), regional interactions enhance the efficiency of FEWS by improving social preparedness through indirect experiences with floods. These findings suggest that as regional heterogeneity increases, it becomes increasingly vital for forecasters to consider social aspects (e.g., people’s experiences, trust, and interactions) when establishing a socially efficient FEWS. The refined model will be valuable to forecasters in designing effective FEWS in real-world situations.

Evaluating flood early warning system and public preparedness and knowledge in urban and semi-urban areas of Johor, Malaysia: Challenges and opportunities

Flood Early Warning System (FEWS) play a crucial role in mitigating the impacts of floods by providing timely information to the public, which is particularly important for flood-prone areas in the Malaysian State of Johor that have suffer significant damages and disruptions almost every year. This research presents an assessment into the current state of FEWS through expert interviews and consultations with the concerned department to understand the management and institutional status and challenges in employing FEWS. In addition, a total of 106 structured questionnaires were administered as part of the survey research to a sample of residents in urban Johor Bahru and semi-urban Kota Tinggi, two regions vulnerable to floods, to collect data on their level of preparedness and knowledge, and their perceived effectiveness of FEWS. The analysis revealed that 69.8 % of the respondents did not have any knowledge on the FEWS in their place, and 60.5 % respondents turn to social media to gain information on flood warning, occurrence and forecast. This study also evaluates the opportunity for the messaging application as an additional support system for FEWS. The research highlights and identifies the challenges and barriers to accessing and utilising FEWS. The study calls for the incorporation of messaging application, preferable WhatsApp to transmit early warnings to the end-users and to use their feedback in designing/redesigning the FEWS. The findings can inform policy and decision-making processes aimed at improving flood preparedness and response strategies, and contribute to the existing literature on FEWS in Malaysia and other similar regions.

The IoT-Based Early Warning System for Detecting High Tide Floods (ROB-EWS) in Tambak Lorok, Semarang Indonesia

Background and Objectives: The Tambaklorok fishing village area, located on the northern coast of Semarang City, Indonesia, is susceptible to sea level fluctuations, which often lead to tidal flooding and seawater encroaching into residential areas. Consequently, a seawater flood detector (rob) is needed for effective disaster mitigation for affected communities. This study aims to implement a disaster mitigation system by installing an IoT-based Seawater Flood (Rob) Early Warning System to reduce disaster risks and their impacts on the community. Methods: The IoT-based Early Warning System is designed to detect floods by monitoring sea level height parameters. Results: IoT-based devices measure sea level using ultrasonic sensors, with the readings processed by a microprocessor. The data is then periodically analyzed and transmitted to an application in real-time. This technology allows for real-time information dissemination through web applications and alerts in the form of sirens. These warnings enable residents to evacuate and move to safer areas. The siren alerts are categorized into two levels: Alert 1 indicates that seawater is approaching the land boundary, while Alert 2 signals that water has begun to enter residential areas. Additionally, designated evacuation zones will guide affected communities to safer areas. Conclusion: The installation of an IoT-based Early Warning System delivers real-time information to help mitigate the impact of rising sea levels (rob) in the Tambak Lorok fishing village community.

Current status and challenges in operating flood early warning systems at the local level in Japan

Flood early warning systems (FEWS) are crucial in reducing flood loss and damage, especially under increasing flood risks due to climate change. Currently, there is limited literature holistically investigating all components of FEWS, namely Risk knowledge, Monitoring and Forecasting, Warning Dissemination, and Preparedness and Response Capabilities. There is also a lack of in-depth understanding of FEWS operation at the local level where local governments play a crucial role. This study conducted a nation-wide survey targeting Japanese municipalities (n = 350) to investigate the current status and challenges of FEWS operations at the local level and the relationships between these elements. The results indicated that while progress in FEWS operation varies in municipalities, they are encountering different challenges in each element of the system. These include a lack of human resources and financial resources, difficulties in risk assessment and data acquisition, limitations of dissemination methods to reach vulnerable, and limited understanding of public perception. This study is the first to investigate all four key elements of FEWS at the local level and the relationships of its key factors, providing useful implications for policy directions to enhance FEWS implementation. All key elements of FEWS positively correlate, suggesting that improving one component can improve the whole system. Government should also strengthen progress in vulnerability assessment, particularly incorporating socio-economic changes as these have the strongest influence on preparedness and response capability of the local areas.

Integrating Flood Early Warning System (FEWS) for Optimizing Small Hydropower Sites: A West Java Case Study

Integrating Flood Early Warning System (FEWS) for Optimizing Small Hydropower Sites: A West Java Case Study

Telegram bot-based Flood Early Warning System with WSN Integration

Indonesia experiences frequent flooding, with data from the National Disaster Management Agency (BNPB) revealing that floods account for 41% of all natural disasters (1,441 incidents) recorded in 2021. These floods cause significant property damage and casualties. To address this challenge, we have developed a prototype flood early warning system. This system utilizes ultrasonic sensors for real-time water level detection. Sensor data is transmitted to designated personnel through a website interface. Additionally, the system leverages a Telegram bot to deliver flood early warnings directly to the community residing in flood-prone areas. The sensor data comparison test yielded an error rate of only 0.6175% with an average difference of 1 cm, demonstrating the system's accuracy and functionality. Furthermore, a notification test conducted ten times achieved 100% accuracy. The Telegram bot successfully sent text message alerts (alert 1, alert 2, alert 3) with an average delivery time of 4.07 seconds. This prototype offers a promising solution for flood mitigation. By providing real-time water level data and issuing timely alerts via a user-friendly Telegram bot, the system empowers communities to prepare for potential flooding and minimize associated risks.

Development of measured hydrodynamic information-based flood early warning system for small streams

In general, small stream basins, characterized by narrow channels and steep slopes, face heightened vulnerability to climate change-induced flooding, posing challenges for evacuation procedures. With the increasing intensity of floods and typhoons in recent years, urgent measures are necessary to mitigate damage in such areas. This research endeavors to address these challenges by developing a novel small stream flood early warning system (SSFEWS) tailored to small streams and piloting its application. The proposed system integrates real-time hydrodynamic data collection, flood probability forecasting, and proactive warning issuance through an amalgamation of IoT-based sensor networks, statistical models leveraging measurement data, a robust constrained nonlinear optimization algorithm (RCNOA), and four-parameter logistic method (4PL). Moreover, system accuracy and reliability are enhanced by an automated iterative process that continuously refines forecasting model parameters via a user-defined rainfall-discharge nomograph and rating curve using RCNOA and 4PL. The developed SSFEWS is expected to contribute to the safety of the community as well as prevent possible small stream-related casualties by enabling efficient disaster response.© 2024 Elsevier Ltd. All rights reserved.

Integration of UH SUH, HEC-RAS, and GIS in Flood Mitigation with Flood Forecasting and Early Warning System for Gilireng Watershed, Indonesia

A flood forecasting and early warning system is critical for rivers that have a large flood potential, one of which is the Gilireng watershed, which floods every year and causes many losses in Wajo Regency, Indonesia. This research also introduces an integration model between UH SUH and HEC-RAS in flood impact analysis, as a reference for flood forecasting and early warning systems in anticipating the timing and occurrence of floods, as well as GIS in the spatial modeling of flood-prone areas. Broadly speaking, this research is divided into four stages, namely, a flood hydrological analysis using UH SUH, flood hydraulic tracing using a 2D HEC-RAS numerical model, the spatial modeling of flood-prone areas using GIS, and the preparation of flood forecasting and early warning systems. The results of the analysis of the flood forecasting and early warning systems obtained the flood travel time and critical time at the observation point, the total time required from the upstream observation point to level 3 at Gilireng Dam for 1 h 35 min, Mamminasae Bridge for 4 h 35 min, and Akkotengeng Bridge for 8 h 40 min. This is enough time for people living in flood-prone areas to evacuate to the 15 recommended evacuation centers.

Worldwide Research Trends and Networks on Flood Early Warning Systems

This review paper examined the global landscape of research on continental flood early warning systems (EWS), shedding light on key trends, geographic disparities, and research priorities. Continental floods stand as one of the most pervasive and devastating disasters worldwide, necessitating proactive measures to mitigate their impact. Drawing upon a comprehensive analysis of the scholarly literature indexed in the Web of Science repository, this study unveiled significant patterns in EWS research. While the emphasis on flooding is evident, a considerable portion of research focuses on precipitation as a variable and modeling approaches. Furthermore, the influence of climate change emerges as a prominent theme, though distinguishing between climate change and variability remains a crucial area for exploration. Geographically, Europe, particularly England and Italy, dominates research efforts in flood related EWS. Conversely, the limited representation of Central America and other regions such as Asia and Oceania, underscores the need for greater attention to regions facing significant flood risks. Importantly, the concept of total link strength emerges as a valuable metric, highlighting collaborative networks established by European countries and the United States. Based on these findings, recommendations are proposed to enhance the inclusivity and effectiveness of flood related EWS research, including a broader consideration of socio-economic factors, fostering collaboration among researchers from diverse regions, and prioritizing initiatives to strengthen research capacities in vulnerable areas. Ultimately, this study provides valuable insights for policymakers, researchers, and practitioners seeking to advance flood risk management strategies on a global scale.

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

Forecasting of compound ocean-fluvial floods using machine learning

Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or pluvial, without considering the compound nature of flood events. In this paper, we developed a novel approach for modelling and forecasting compound coastal-fluvial floods using a two-step framework. In step one, a hydrodynamic model is used to simulate floodwater propagation; while in step two, machine learning (ML) models are used to generate flood forecasts. The architecture of hydrodynamic-ML forecasting system incorporates a hydrodynamic model covering a specific domain, with individual ML models trained for each pixel. In total 7 ML models including: Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) were applied in this study. Forecasting compound floods is achieved using two sets of inputs: timeseries of river discharges in the upstream fluvial section and downstream ocean water levels in the coastal areas. The accuracy of the flood forecasting system is demonstrated for Cork City, Ireland; and modelling performance was evaluated using several statistical tools. Results show that the proposed models can provide reliable estimates of flood inundation and associated water depths. Overall, the RBF model exhibits the best performance. Despite the complexity of compound multi-driver floods, this study shows that the coupled hydrodynamic-ML approach can forecast coastal-fluvial flood with limited hydraulic and hydrological input data. This system overcomes the limitations of traditional hydrodynamic model-based systems where trade-offs between the always competing numerical model accuracy and computational time prohibit the model to be used for short-term flood forecasting. Once trained, the ML component of the coupled system can perform flood forecasting in near real-time, potentially integrating into a flood early warning system. Accurate flood forecasting has a wide range of positive societal impacts, including improved flood preparedness, increased confidence, better resource allocation, reduced flood damage, and potentially even flood prevention.

Water Level Monitoring for Flood Early Mitigation Based on Internet of Things (IoT)

Flooding is a disaster that often threatens communities, especially in high-risk areas. It is not surprising that this disaster causes many losses to the community morally and materially. This is because the area is on the riverbank and is the lowest point. The purpose of this final project is to design a flood early warning system as a water level monitoring to minimize the losses caused by floods with a water level monitoring tool to provide early warning to the community in the event of a flood. This water level monitoring tool utilizes solar panels as an energy source, where this tool uses ultrasonic sensors as water level detection and rain sensors as rainfall detection. This tool requires 28.6 Watts of power to power the system and the accuracy of the ultrasonic sensor is 99.94% with an average ultrasonic sensor error of 0.05%. This water level monitoring tool will help improve the flood early warning system, thus enabling the community to take appropriate mitigation steps before floods reach dangerous levels. Thus, losses due to flooding can be reduced, and assets can be restored, communities can be better protected from the threat of flooding.

Instrumentation for Monitoring and Early Warning of Tidal Flood Using ESP32S2 Microcontroller and A01NYUB Ultrasonic Sensor in Tambakrejo, Semarang

Abstract A tidal flood early warning system is crucial to minimize the impact and loss in tidal flood-prone areas. This research proposes an instrumentation and information system for tidal flood early warning monitoring by optimizing tidal data in the local area. The efficiency of the instrumentation depends on the accuracy of the sensors installed. This research presents the testing and application of The A01NYUB ultrasonic sensor with The ESP32S2 microcontroller for real-time technology-based water level monitoring and The Internet of Things concept. The collected sensor data is analyzed with an algorithm that has also been proposed in this research. The results show that the ultrasonic sensors can provide a reliable and adequate tide monitoring scheme with an accuracy of RMSE value of 0.08943 and bias value of – 2×10−16. The developed system provides early warning when the sea level reaches 260-280 cm with the MSL datum reference value range of -20 cm to 40 cm. The system has been tested at the Fisherman Boat Harbor, Tambakrejo, Semarang, Central Java, Indonesia. The test has shown the system’s accuracy and has proven that the developed system will help minimize the impact of the tidal flood disaster.

Early Flood Warning System

Early Flood Warning System

Enhancing Flood Risk Management: A Comprehensive Review on Flood Early Warning Systems with Emphasis on Numerical Modeling

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience.

Flood early warning system at Jakarta dam using internet of things (IoT)-based real-time fishbone method to support industrial revolution 4.0

This research aims to develop an effective flood early warning system to provide timely information to the public and support the government in disaster management. The Raspberry Pi mini-computer functions as the central control, collecting data from the Water Level Sensor to measure water height, the Ultrasonic Sensor for further monitoring, the DHT11 Sensor to monitor temperature and humidity, and a Buzzer as an audible warning device. The research method involves review of the literature and data acquisition from related journals. These data are utilized to design an Internet of Things (IoT)-based flood detection tool with the Raspberry Pi minicomputer as the main controller. The system can be implemented in vulnerable locations such as reservoirs, sluice gates, and rivers, as part of the Smart City and Smart Environment concepts. The test results indicate that the developed early warning system, integrating the Raspberry Pi minicomputer, the Water Level Sensor, the the Ultrasonic Sensor DHT11 Sensor, and Buzzer, approaches perfection. Real-time information is transmitted through the Twitter social media platform, which is shown to be more effective than manual notifications. The system can provide accurate early warnings, reduce flood-related damages, and positively contribute to flood prevention and disaster management efforts. This research is expected to make a significant contribution to improving the community and government preparedness for future flood disasters.