Flood Warning Research Articles

A standards-based approach to enhancing interoperability of low-cost industrial IoT flood sensors for transportation system resilience

Transportation infrastructure assets are particularly vulnerable to natural hazards, which are becoming more frequent and severe due to climate change and extreme weather conditions. Floods and flash floods are among the deadliest natural hazards, accounting for 50% of vehicle-related fatalities. This underscores the need for timely transportation flood detection systems adoption. However, current flood detection technologies are inadequate in terms of coverage, speed, geographical specificity, and interoperability, making it difficult for emergency managers to respond effectively to flood events. To address this issue, we propose a high-resolution network of low-cost Industrial Internet of Things (IIoT) sensing devices deployed at flood-prone transportation assets. These sensors collect location-specific data, which is then published in standardized formats, interfaces, and protocols, enabling other systems to generate flood forecasts, nowcasts, and warnings. A framework for Incident Management Systems (IMS) was also discussed to highlight the need for system interoperability during disaster management operations. Our solution employs standards-based interoperability, using the OIIE™ OpenO&M ecosystem architecture, to enable seamless interaction between interdependent systems and manages the risk to critical transportation infrastructure. The technology was tested at a microscale level to evaluate its performance. The model architecture supports scalable systems of systems interoperability for standardized use cases and common asset classes used in transportation, energy, facilities, and other critical infrastructure.

Predictive uncertainty assessment in flood forecasting using quantile regression

Abstract Floods and their associated impacts are topics of concern in land development planning and management, which call for efficient flood forecasting and warning systems. The performance of flood warning systems is affected by uncertainty in water level forecasts, which is due to their inability to measure or calculate a modeled value accurately. Predictive uncertainty is an emerging type of uncertainty modeling technique that emphasizes total uncertainty quantified as a probability distribution conditioned on all available knowledge. Predictive uncertainty analysis was done using quantile regression (QR) for machine learning-based flood models – Hybrid Wavelet Artificial Neural Network model (WANN) and Hybrid Wavelet Support Vector Machine model (WSVM) for different lead times. Comparing QR models of WANN and WSVM revealed that the slope, intercept, spread of forecast, and width of confidence band of the WANN model are more for each quantile indicating more uncertainty as compared to the WSVM model. In both models, with an increase in lead time, uncertainty has shown an increasing trend as well. The performance evaluation of inference obtained from QR models was evaluated using uncertainty statistics such as prediction interval coverage probability, average relative interval length (ARIL), and mean prediction interval (MPI).

Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia

Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training.

Intelligent flood forecasting and warning: a survey

Accurately predicting the magnitude and timing of floods is an extremely challenging problem for watershed management, as it aims to provide early warning and save lives. Artificial intelligence for forecasting has become an emerging research field over the past two decades, as computer technology and related areas have been developed in depth. In this paper, three typical machine learning algorithms for flood forecasting are reviewed: supervised learning, unsupervised learning, and semi-supervised learning. Special attention is given to deep learning approaches due to their better performance in various prediction tasks. Deep learning networks can represent flood behavior as powerful and beneficial tools. In addition, a detailed comparison and analysis of the multidimensional performance of different prediction models for flood prediction are presented. Deep learning has extensively promoted the development of real-time accurate flood forecasting techniques for early warning systems. Furthermore, the paper discusses the current challenges and future prospects for intelligent flood forecasting.

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

Floods are the most common natural disaster in several countries throughout the world. Flooding has a major impact on people's lives and livelihoods. The impact of flood disasters on human lives can be mitigated by developing effective flood forecasting and prediction models. The majority of flood prediction models do not take all flood-causing factors into account when they are designed. It is difficult to collect and handle some of these flood-causing variables since they are heterogeneous in nature. This paper presents a new big data architecture called Data Lake, which can ingest and store all important flood-causing heterogeneous data sources in their raw format for machine learning model creation. The statistical relevance of important flood producing factors on flood prediction outcome is determined utilizing inferential statistical approaches. The outcome of this research is to create flood warning systems that can alert the public and government officials so that they can make decisions in the event of a severe flood, reducing socioeconomic loss.•Flood causing factors are from heterogeneous sources, so there is no big data architecture for handling variety of data sources.•To provide data architectural solution using data lake for collecting and analysing heterogeneous flood causing factors.•Uses inferential statistical approach to determine importance of different flood causing factors in design of efficient flood prediction models.

Evaluation of the Rio de Janeiro State Flood Warning System: A Case Study for the Hydrographic Region of the Médio Paraíba do Sul (RJ), Brazil

This work is a case study for the Hydrographic Region of the Médio Paraíba do Sul located in the State of Rio de Janeiro (Brazil), which has natural disasters, such as floods, as the most recurrent. Due to the social, economic and environmental impacts that these disasters cause, this research aim to analyze the history of alerts issued by the Flood Warning System (FWS) and to assess its efficiency. Through the retro-analysis of Alert Trigger Events (ATE) that occurred in the monitored rivers, investigations were also carried out in the hydrological and meteorological scope. The results identified that the Attention stage belonging to the operational protocol significantly reduces the efficiency of the FWS about sending alerts, causing high false alarm rate. Regarding the influence that variation in the type of data transmission has on the Operational Protocol in RH-III, it is considered that the time interval between data transmission and their availability on the State Environmental Institute (SEI) erver generates a false alarm, however the FWS is still efficient. Barra Mansa River is the only one that has flood events, which raises questions about the representativeness of the other stations spread across RH-III. In addition, 60% of alerts issued for RH-III were related to episodes of Cold Fronts and South Atlantic Convergence Zones. These information helps improve the description of the flood events in the region. Rainfall Anomaly Index (RAI) identified periods that the rainfall rate was below the climatological average, indicating a drier environment, but with alerts issued.

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

Collecting, storing, and providing access to Internet of Things (IoT) data are fundamental tasks to many smart city projects. However, developing and integrating IoT systems is still a significant barrier to entry. In this work, we share insights on the development of cloud data storage and visualization tools for IoT smart city applications using flood warning as an example application. The developed system incorporates scalable, autonomous, and inexpensive features that allow users to monitor real-time environmental conditions, and to create threshold-based alert notifications. Built in Amazon Web Services (AWS), the system leverages serverless technology for sensor data backup, a relational database for data management, and a graphical user interface (GUI) for data visualizations and alerts. A RESTful API allows for easy integration with web-based development environments, such as Jupyter notebooks, for advanced data analysis. The system can ingest data from LoRaWAN sensors deployed using The Things Network (TTN). A cost analysis can support users’ planning and decision-making when deploying the system for different use cases. A proof-of-concept demonstration of the system was built with river and weather sensors deployed in a flood prone suburban watershed in the city of Charlottesville, Virginia.

Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China

Study Region: Nine mountain catchments (5 in northern and 4 in southern China).Study Focus: Flash floods in small mountain catchments are some of the most frequent and most destructive natural disasters of China. Mixed runoff hydrological model is a popular method for flood warning and forecasting. However, the key factors affecting runoff components in hydrological model parameter calibration need to be further studied. In this study, four different overland flow routing techniques are implemented into a semi-distributed mixed runoff hydrological model system (MRHMS), which are the triangle unit hydrograph (TUH), geomorphological instantaneous unit hydrograph (GIUH), geomorphoclimatic instantaneous unit hydrograph (GcIUH) and kinematic wave (KW). MRHMS-KW is the previously developed spatiotemporally-mixed runoff (SKBY) model.New hydrological insights for the region: The study finds that sensitivity parameters of the MRHMS in northern and southern China are related to their main runoff components. Different parameter orders in automatic calibration of MRHMS result in different model performances, and the suggested parameter orders are consistent with the sensitivity of parameters in northern and southern China. Soil moisture and primary runoff components in northern and southern China differ significantly. Only MRHMS-GcIUH can achieve similar performance in northern and southern catchments. MRHMS-GcIUH therefore enhances our understanding of runoff formation mechanisms in the heterogeneous hydroclimatic landscape of China and is a promising tool for a nationwide flood prediction and flood warning system.

Neural networks for fast fluvial flood predictions: Too good to be true?

AbstractAccurate models with low computational times are required to predict the consequences of fluvial floods in real‐time. Even though detailed hydraulic models can predict flood water levels and corresponding inundation extents with high accuracy, their computational times limit their applicability to being used as a flood early‐warning system. Therefore, conceptual models were developed in the literature for many years. These types of models do not attempt to represent the complex dynamic flood generation processes but are based on simplified hydraulic concepts, generally only using a digital elevation model as input. However, a shift in this research field is currently present from conceptual models to data‐driven models, and more specifically to neural networks. This paper discusses the benefits and drawbacks of both modelling approaches and speculates which method is most promising to be used as a flood warning system to predict the consequences of fluvial floods in real‐time.

Analysis of Forecast and Early Warning of Flood in Medium and Small Rivers

Flooding of small and medium rivers is caused by environmental factors like rainfall and soil loosening. With the development and application of technologies such as the Internet of Things and big data, the disaster supervision and management of large river basins in China has improved over the years. However, due to the frequent floods in small and medium-sized rivers in our country, the current prediction and early warning of small and medium-sized rivers is not accurate enough; it is difficult to realize real-time monitoring of small and medium-sized rivers, and it is also impossible to obtain corresponding data and information in time. Therefore, the construction and application of small and medium-sized river prediction and early warning systems should be further improved. This paper presents an analysis and discussion on flood forecasting and early warning systems for small and medium-sized rivers in detail, and corresponding strategies to improve the effect of forecasting and early warning systems are proposed.

Critical Failure Factors of Flood Early Warning and Response Systems (FEWRS): A Structured Literature Review and Interpretive Structural Modelling (ISM) Analysis

Flood warning and response systems are essential components of risk reduction strategies with the potential to reduce loss of life and impact on personal assets. However, recent flood incidents have caused significant loss of human lives due to failures in current flood warning and response mechanisms. These failures are broadly related to policies concerning, and governance aspects within, warning generation, the behaviour of communities in responding to early warnings, and weaknesses in associated tools and technologies used in communicating early warnings and responding. Capturing critical failure factors affecting flood warning and response systems can provide opportunities for making corrective measures and for developing a more advanced and futuristic system for early flood warnings. This paper reports the findings of a structured review that was conducted to identify critical failure factors in flood early warning and response systems. This study found twenty-four critical failure factors (CFFs). The interpretive structural modelling (ISM) approach conducted in this study resulted in identifying four different types of failure factors (autonomous, dependent, linkage, and independent) with varying dependence and driving powers. Analysis shows that governance, leadership, finance, standard operating procedures (SoP), and community engagement are the most dominating factors with the highest driving factor, which can overcome other dependent factors. The outcome of this review could be helpful for policymakers and practitioners in overcoming failure factors and implementing effective early warning and response systems.

Estimation of Real-Time Rainfall Fields Reflecting the Mountain Effect of Rainfall Explained by the WRF Rainfall Fields

The effect of mountainous regions with high elevation on hourly timescale rainfall presents great difficulties in flood forecasting and warning in mountainous areas. In this study, the hourly rainfall–elevation relationship of the regional scale is investigated using the hourly rainfall fields of three storm events simulated by Weather Research and Forecasting (WRF) model. From this relationship, a parameterized model that can estimate the spatial rainfall field in real time using the hourly rainfall observation data of the ground observation network is proposed. The parameters of the proposed model are estimated using eight representative pixel pairs in valleys and mountains. The proposed model was applied to the Namgang Dam watershed, a representative mountainous region in the Korea, and it was found that as elevation increased in eight selected pixel pairs, rainfall intensity also increased. The increase in rainfall due to the mountain effect was clearly observed with more rainfall in high mountainous areas, and the rainfall distribution was more realistically represented using an algorithm that tracked elevation along the terrain. The proposed model was validated using leave-one-out cross-validation with seven rainfall observation sites in mountainous areas, and it demonstrated clear advantages in estimating a spatial rainfall field that reflects the mountain effect. These results are expected to be helpful for flood forecasting and warning, which need to be calculated quickly, in mountainous areas. Considering the importance of orographic effects on rainfall spatial distribution in mountainous areas, more storm events and physical analysis of environmental factors (wind direction, thermal cycles, and mountain slope angle) should be continuously studied.

Application of AI-Based Models for Flood Water Level Forecasting and Flood Risk Classification

Owing to global climate change, the frequency of disasters has increased twelve-fold, with a corresponding approximately seventeen-fold increase in economic damages over the past six decades. Notably, severe flood damage has been occurring in Asia along the Pacific coast due to extreme weather events, including torrential rains and typhoons, which have been becoming increasingly frequent and prolonging the rainy season. In the Eastern Visayas region, the management and monitoring facilities for flood observation data, as well as the forecasting and warning systems suitable for the local area, are insufficient. The warning system introduced through overseas grants is limited in operation in some areas of the city. Furthermore, although an organization has jurisdiction over flood forecasting and warning, the system's operation is not systematic and is limited. Additionally, there is a shortage of technical manpower. In this study, we utilized deep learning models to forecast flood water levels in the CarayCaray Basin on Biliran Island, located in Eastern Visayas, the Philippines. Additionally, a flood risk classification was applied to evaluate the degree of risk associated with the predicted water levels. The predicted water levels for each model were compared with the observed water level data. The evaluation of the predictive performance of each model resulted in an NRMSE value of 9.48. Moreover, the accuracy of the DNN model was found to be the best among the flood water level prediction models. To implement the flood risk classification, we utilized extreme gradient boosting, random forest, and decision tree models. The application of these models resulted in an F1-score of 0.92 for the extreme gradient boost model, which exhibited the highest accuracy. With an increasing need for disaster (flood) management, AI-based predictive models are anticipated to reduce the damage caused by natural disasters and enhance disaster mitigation systems. Real-time collection of rainfall and water level data enables continuous learning. Furthermore, if a clear flood warning based on learned flood level patterns is issued, preemptive measures can be taken before intense flood damage occurs.

Conceptual and quantitative categorization of wave-induced flooding impacts for pedestrians and assets in urban beaches

Beaches combined with sloping structures are frequently the first element of defense to protect urban areas from the impact of extreme coastal flooding events. However, these structures are rarely designed for null wave overtopping discharges, accepting that waves can pass above the crest and threat exposed elements in hinterland areas, such as pedestrians, urban elements and buildings, and vehicles. To reduce risks, Early Warning Systems (EWSs) can be used to anticipate and minimize the impacts of flooding episodes on those elements. A key aspect of these systems is the definition of non-admissible discharge levels that trigger significant impacts. However, large discrepancies in defining these discharge levels and the associated impacts are found among the existing methods to assess floodings. Due to the lack of standardization, a new conceptual and quantitative four-level (from no-impact to high-impact) categorization of flood warnings (EW-Coast) is proposed. EW-Coast integrates and unifies previous methods and builds on them by incorporating field-based information. Thus, the new categorization successfully predicted the impact level on 70%, 82%, and 85% of the overtopping episodes affecting pedestrians, urban elements and buildings, and vehicles, respectively. This demonstrates its suitability to support EWSs in areas vulnerable to wave-induced flooding.

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

Knowledge of the stage-discharge rating curve is useful in designing and planning flood warnings; thus, developing a reliable stage-discharge rating curve is a fundamental and crucial component of water resource system engineering. Since the continuous measurement is often impossible, the stage-discharge relationship is generally used in natural streams to estimate discharge. This paper aims to optimize the rating curve using a generalized reduced gradient (GRG) solver and the test the accuracy and applicability of the hybridized linear regression (LR) with other machine learning techniques, namely, linear regression-random subspace (LR-RSS), linear regression-reduced error pruning tree (LR-REPTree), linear regression-support vector machine (LR-SVM) and linear regression-M5 pruned (LR-M5P) models. An application of these hybrid models was performed and test to modeling the Gaula Barrage stage-discharge problem. For this, 12-year historical stage-discharge data were collected and analyzed. The 12-year historical daily flow data (m3/s) and stage (m) from during the monsoon season, i.e., June to October only from 03/06/2007 to 31/10/2018, were used for discharge simulation. The best suitable combination of input variables for LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models was identified and decided using the gamma test. GRG-based rating curve equations were found to be as effective and more accurate as conventional rating curve equations. The outcomes from GRG, LR, LR-RSS, LR-REPTree, LR-SVM, and LR-M5P models were compared to observed values of daily discharge based on Nash Sutcliffe model efficiency coefficient (NSE), Willmott Index of Agreement (d), Kling-Gupta efficiency (KGE), mean absolute error (MAE), mean bias error (MBE), relative bias in percent (RE), root mean square error (RMSE) Pearson correlation coefficient (PCC) and coefficient of determination (R2). The LR-REPTree model (combination 1: NSE = 0.993, d = 0.998, KGE = 0.987, PCC(r) = 0.997, and R2 = 0.994 and minimum value of RMSE = 0.109, MAE = 0.041, MBE = −0.010 and RE = −0.1%; combination 2; NSE = 0.941, d = 0.984, KGE = 0. 923, PCC(r) = 0. 973, and R2 = 0. 947 and minimum value of RMSE = 0. 331, MAE = 0.143, MBE = −0.089 and RE = −0.9%) performed superior to the GRG, LR, LR-RSS, LR-SVM, and LR-M5P models in all input combinations during the testing period. It was also noticed that the performance of the alone LR and its hybrid models (i.e., LR-RSS, LR-REPTree, LR-SVM, and LR-M5P) was better than the conventional stage-discharge rating curve, including the GRG method.

Monitoring Ketinggian Air dan Curah Hujan Dalam Early Warning System Bencana Banjir Berbasis IoT

Floods are natural disasters where there is an excessive volume of water that dampens the land. If left unchecked, flooding can bring disease, cause environmental damage and hinder people's mobility. Therefore, we need a system that can provide early warning to the community before a flood occurs. As technology develops, we can monitor the water level in an area to anticipate flooding. This concept is called the Early Warning System (EWS). IoT can help with real-time and continuous monitoring and warning of floods. IoT can also monitor air levels remotely. The MQTT protocol research also uses water level and rainfall sensors as data for flood detection. This system will detect water level and rainfall in real time. If the water level and rainfall reach a certain limit, the system will provide a warning to local residents via chatbot. There are 3 types of alerts in this system. Monitoring results are also displayed via the dashboard and sensor readings will be stored in the cloud database. The evaluation results show that the designed system can work well in providing flood early warning. Bots on Telegram have also sent notifications with an average delay of 0.561 seconds.

Community perspectives to improve flood management and socio-economic impacts of floods at Central Indus River, Pakistan

Pakistan suffers from floods in the main rivers of the Indus River System due to disturbed weather patterns during monsoon and snowmelt. The study aims to identify the community perspectives on the socioeconomic impacts of floods in the Central Indus River (Punjab and Sindh), Pakistan. A questionnaire-based survey was conducted in three major biodiversity hotspot areas: Taunsa Barrage Wildlife Sanctuary, Indus River Dolphin Reserve, and Central Indus Wetlands Complex. The questionnaire identified people's knowledge, practice, and attitude regarding floods in the area. The results were interesting as they depict that the community is fully aware of the causes and impacts of floods and migratory measures. Loss of agricultural land, displacement of the human population, and animal structures were the most common impacts on people. Government departments issued flood warnings in the area to implement flood prevention plans (movement to the safe zone like protection bund). Still, due to lack of information resources, people needed more time to get information which caused significant losses. Non-governmental organizations (NGOs) in the area played an important role in minimizing the after-effects of floods and rehabilitating the area. This study will help to improve policies for disaster risk reduction.

Real-Time Flood Forecasting and Warning: A Comprehensive Approach toward HCI-Centric Mobile App Development

This article discusses the design, development, and usability assessment of a mobile system for producing hydrological predictions and sending flood warnings in response to the desire for human-centered technology to better the management of flood occurrences. Our work acts as a bibliographic reference for understanding what others have attempted and found, as well as gives an integrated set of recommendations. Furthermore, our guidelines offer guidance to aid in the design of mobile GIS-based hydrological models for mobile devices. We concentrate on the full design of a human–computer interaction framework for an effective flood prediction and warning system. In addition, we analyze and address the current user needs and requirements for building a user interface for mobile real-time flood forecasting in a methodical manner. Although a functional prototype was created, the primary objective of this research was to comprehend the complexity of possible users’ demands and actual use situations in order to solve the problem of comparable systems being difficult to use. After consulting with possible consumers, application design standards were established and implemented in the initial prototype. Focusing on user demands and attitudes, special consideration was given to the usability of the mobile interface. To develop the application, a variety of assessment methods are added. The conclusion of the examination was that the system is efficient and effective.

A data‐oriented strategy to support water resource managers and researchers

AbstractGiven the wide diversity of data services provided to national water management agencies, the Group on Earth Observations (GEO) in collaboration with the Committee on Earth Observation Satellites (CEOS) developed the approach described in the report, Implementing the GEOSS Water Strategy—From Observations to Decisions to develop more coherent and equitable data services for water management through the use of Earth observations. Among other water resource issues, it recognized the need to enhance data‐enriched water management services to support decision making related to drought monitoring, flood warning, tracking and improving sustainable development and monitoring and ameliorating the impacts of climate change. Needs associated with the Strategy's four themes: improved data acquisition for essential water variables, research and product development, interoperability and coordination, and capacity development and decision support, are reviewed. Responses to the recommendations have been undertaken by GEO, led by its Global Water Sustainability (GEOGloWS) initiative which includes NASA contributions, CEOS, and the Global Terrestrial Network for Hydrology (GTN‐H). Progress on the themes is reviewed and benefits of these developments for international and US water management are identified. The commentary concludes with a summary of what has been achieved, what remains to be done, and the priority focus areas for implementation in the final year of the Strategy.

Research on Water Level Anomaly Data Alarm Based on CNN-BiLSTM-DA Model

With frequent extreme rainfall events caused by rapid changes in the global climate, many cities are threatened by urban flooding. Timely issuance of flood warnings can help prepare for disasters and minimize losses caused by floods. In this study, we propose a method based on a convolutional neural network-bidirectional long short-term memory-difference analysis (CNN-BiLSTM-DA) model for water level prediction analysis and flood warning. The method calculates and analyzes the difference sequence between water level monitoring values and water level prediction values, compares historical flood data to determine the alarm threshold for abnormal water level data, and achieves real-time flood warnings to provide technical references for flood prevention and mitigation. Taking Yancheng city, a low-lying city located in the plain area of Jiangsu Province in China, as an example, this study verifies the accuracy of the CNN-BiLSTM model in water level prediction, which can achieve an accuracy rate above 95%. This provides a reliable data basis for further determination of warning thresholds using the DA model. The CNN-BiLSTM-DA model achieves an accuracy rate of 85.71% in flood warnings without any missed reports, demonstrating that this method has scientific, practical, and accurate features in addressing flood warning issues.