Flood Warning System Research Articles

Effects of model complexity on karst catchment runoff modeling for flood warning systems

Effects of model complexity on karst catchment runoff modeling for flood warning systems

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.

Effectiveness of flood early warning for the Jamuna char-dwellers at Sirajganj District in Bnagladesh

Floods, a pervasive global natural disaster, are a recurrent challenge in Bangladesh, causing substantial damage to property, infrastructure, and lives. The country has implemented a flood warning system since 1972, focusing on people, property, and cost-effectiveness. However, the efficacy of this system relies on accurate and timely dissemination of warnings and prompt responses from at-risk communities to mitigate losses and damages. This study evaluates the effectiveness of the flood early warning systems at the grassroots level in Sirajganj District, specifically in two purposively selected villages within the Kaijuri Union. One village is situated along the unprotected bankline, while the other is in the island char area. Data was gathered from 116 households through interviews, focus group discussions, and key informant interviews. Results of the Multi-criteria analysis reveal that the system is moderately effective for the bankline village (Kaijuri) with a score of 0.400, but considerably less effective for the char-land village (Thutia) with a score of 0.174. Inadequate communication links contribute to delayed and unreliable information, leading to higher losses and damages. The study emphasizes the necessity of building community trust by involving them in the planning and management of flood warning systems for more effective risk reduction.

Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability

Assessing Driver’s Trust, Compliance, and Reliance in an Automated Flood Warning System: Effects of Error Types and System Reliability

Runoff prediction based on the IGWOLSTM model: Achieving accurate flood forecasting and emergency management

With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people’s lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes.

Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations

ABSTRACT Flooding poses a severe threat to communities and infrastructure worldwide, which requires advanced flood forecasting warning systems. In this research paper, a real-time flood forecasting and warning system for the Dharoi Dam in the state of Gujarat, India is developed. This novel system combines ensemble techniques and hydrological modeling simulations to enhance flood prediction accuracy and provides a timely warning. The study focuses on critical gaps in the current flood forecasting capabilities, recognizes the need for improved flood management in the region, and builds upon the existing research conducted globally and in India. The real-time flood forecasting and warning system uses information from various sources such as rainfall, river flows, and water level observations. The system enhances the accuracy of flood forecasts with a 1–5-day lead time by utilizing ensemble techniques, which incorporate multiple models and their corresponding forecasts. The 2-day and 3-day lead times combined with postprocessing techniques yield excellent results, as evidenced by the reservoir inflow correlation value of 0.86 and the receiver operating characteristic-area under the curve (ROC-AUC) value of 0.93. This work aims to reduce the impact of floods in this region and can be used by decision-makers as a disaster management tool.

Insights from a topo-bathymetric and oceanographic dataset for coastal flooding studies: the French Flooding Prevention Action Program of Saint-Malo

Abstract. The French Flooding Prevention Action Program of Saint-Malo, France, requires the assessment of coastal flooding risks and the development of a local flood warning system. The first prerequisite is knowledge of the topography and bathymetry of the bay of Saint-Malo; the acquisition of new multibeam bathymetric data was performed in 2018 and 2019 to increase the resolution of the existing topo-bathymetric datasets and to produce two high-resolution (20 and 5 m) topo-bathymetric digital terrain models. Second, the hydrodynamics associated with coastal flooding were investigated through a dense and extensive oceanographic field experiment conducted during winter 2018–2019 using a network of 22 moorings with 37 sensors: the network included 2 directional buoys, 2 pressure tide gauges, 18 wave pressure gauges, 4 single-point current meters, 7 current profilers, and 4 acoustic wave current profilers from mid-depth (25 m) up to the upper beach and the dike system. The oceanographic dataset thus provides an extended overview of the hydrodynamics and wave processes in the bay of Saint-Malo from the coast up to over-flooding and over-topping areas. This dataset helps to identify the physical drivers of the coastal flooding and provides a quantification of their respective contributions. In particular, the wave processes at the foot of the protection structures can be observed: in this macro-tidal environment, during high spring tides, short and infragravity waves propagate up to the protection structures, while the wave set-up remains negligible, and over-topping by sea packs can occur. The combination of high-resolution topo-bathymetric and oceanographic datasets allows the construction, calibration and validation of a wave and hydrodynamic coupled model that is used to investigate flooding processes more deeply and might be integrated into a future local warning system by means of Saint-Malo inter-communality. The topo-bathymetric and oceanographic datasets are available freely at https://doi.org/10.17183/MNT_COTIER_GNB_PAPI_SM_20m_WGS84, https://doi.org/10.17183/MNT_COTIER_PORT_SM_PAPI_SM_5m_WGS84 and https://doi.org/10.17183/CAMPAGNE_OCEANO_STMALO (Shom, 2020a, b, 2021).

Quantifying regional rainfall dynamics in southern India: Unravelling monsoon characteristics and intense precipitation using satellite and observed data records

Rainfall patterns are unpredictable, which has implications for planning irrigation systems, constructing farm ponds and drainage systems, and conserving water and soil. The research findings have been compiled over 36 years, utilizing gridded daily precipitation data from the IMD and PWD (1984–2019). This study investigates the frequency of consecutive rainy days, the yearly rainfall pattern and the precipitation threshold value that triggers the flood. The Mann-Kendall approach is used to analyze trends in the statistical application XLSTAT. According to the trend analysis, rainfall and wet days during the Northeastern Monsoon (NEM) throughout the year show significant and nonsignificant increasing trends at most stations. Statistical or empirical approaches can be used to establish the threshold value of precipitation that causes a flood by examining historical flood episodes and the precipitation that occurred. The Gumbel extreme value distribution method was used to determine the recurrence intervals of days 1, 2, 3, and 4 that are consecutive and equal to or above 5 days. The study's findings will show that rainfall patterns can be used to forecast floods and estimate their return and recurrence intervals. As a result, implementing this research with these precipitation threshold values could help with flood warning systems and the infrastructure required for emergency responses to flooding disasters.

Early Flood Warning System

Early Flood Warning System

Anticipating the impact of glaciers, landslides and extreme weather events on vulnerable hydropower projects and the development of an integrated multi-hazard warning system (IMWS)

The Himalayan river basins offer great potential for hydropower development, but they are also vulnerable to various hazards such as debris flows, landslides, flash floods, glacial lake outburst floods (GLOFs), and landslide lake outburst floods (LLOFs). Despite the regional and global significance of these hazards, there is a lack of information and data on different aspects, including meteorology, hydrology, geology, and seismology. Many hazards often go unnoticed or receive little attention until they start affecting humans and their activities like damage to buildings, infrastructure, and other human-made structures. It is important to recognize that hazards can have severe and long-lasting impacts on society, even when they do not directly affect humans. For example, flash floods can disrupt ecosystems, destroy habitats, and threaten biodiversity and the complexity of climatic influences on both regional and local scales cannot be overlooked. Therefore, we highlight the importance of basin-wise and basin-wide continuous long-term monitoring in the Himalaya. It is also recommended that the highest hydropower projects in the basins should have their network of hydro-meteorological observatories at different altitudes with a provision of real-time data transmission and deployment of a multi-hazard warning system (IMWS) for flash floods.

Application of Parallel Computing on Hybrid Inundation Model, Case Study: Chiayi County Flood 23-24 August 2018

A robust 2D inundation model is needed to support flood warning systems in urban areas. The conventional 2D hydrodynamic model uses shallow water equations as the governing equation and is computationally expensive. Although the models have benefited from parallel computation techniques, some issues remain. As an alternative, many flood models have been developed using different approaches, such as Cellular Automata (CA), DEM-based (DBM), and data-driven models. The hybrid inundation model (HIM) was developed by combining the CA-DBM concepts. The purpose of this study is to implement the parallel computation technique to increase the efficiency of HIM. The model performance was evaluated using the historical flood event in Chiayi County, Taiwan. Results showed that there is no significant difference between HIM and TUFLOW in terms of flood depth estimation, even though TUFLOW included the drainage system within the analysis. These results proved that the drainage system was not working during the event. HIM and TUFLOW give underestimated flood depth prediction compared to the observed data. The main reason because the observed data was obtained from local community testimonies. Hence, there might be many uncertainties in the observed data value. Finally, the parallelization process successfully decreased the computation time of the original HIM. The computation was decreased from 450 to 11 minutes depending on the number of cores used in the simulation.

DEVELOPMENT OF INTERNET OF THINGS (IoT) BASED FLOOD EARLY WARNING TOOLS

This research is research and development (R&D) using a prototype development model. This research aims to develop an early flood warning system using the Telegram application as an early sender of information, if the water level has reached a predetermined limit. Test results: In terms of Functionality and Usability, the results of the functionality feasibility percentage were 100% and as many as 20 respondents or 100% assessed that the tool developed based on the Usability context was at 100% in the very good category. The detection speed test results show that the detection speed on the device is around 2-3 s and the speed of sending information on Oled is around 2-3 s and the speed of sending information to Telegram is 2-3 s.

IoT Based Flood Monitoring and Alerting System

The primary objective of this project is to create a reliable system for ongoing water level monitoring in dams or Wadis, ensuring timely alerts are sent to nearby residents and the Royal Oman Police (ROP) when levels approach hazardous thresholds. Although preventing floods may not always be feasible, implementing an early detection and alert system through continuous monitoring can significantly reduce the impact on communities. This initiative introduces an Internet of Things (IoT)-enabled flood detection and warning system, utilizing a NodeMCU board, an ultrasonic sensor (HC-SR04), temperature and humidity sensors (DHT11), and the Blynk app. The system diligently tracks water levels, transmitting sensor data to the Blynk Server at regular intervals, allowing for global access and oversight. Should water levels exceed predetermined safe limits, an immediate notification is dispatched via email to designated contacts, warning individuals in the vicinity of the dam or Wadi and the ROP of the potential danger.

Assessing precipitation event characteristics throughout North Carolina derived from GPM IMERG data products

The Global Precipitation Measurement (GPM) mission provides near-real time precipitation estimates that can be used for monitoring water supply infrastructure. To better understand the potential use of GPM data products for use in relevant applications, this study examines the performance of the Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation products throughout North Carolina to capture rainfall events. Event characteristics derived from IMERG precipitation products: early (4 h latency), late (14 h latency), and final (3.5 month latency) are compared to characteristics derived from gauges (N = 282) for the period 2000–2021. Results show that the IMERG data products identify events reasonably well (mean event errors of 6.0, 6.8, and 1.7 mm for early, late, and final products; probability of detection: 0.83, 0.83, 0.86; false alarm ratio: 0.20, 0.18, 0.18, respectively). While the final product performs best, the early and late products perform similarly. While additional research is needed to better understand factors leading to under- and over-estimates of event magnitudes, the findings from this study support the potential use of early/late IMERG data products in water supply monitoring or flood warning systems, where rapid estimates of event precipitation characteristics are needed.

Flood prediction based on feature selection and a hybrid deep learning network

Abstract As climates change globally, water-related disasters increase, causing substantial economic losses and safety risks. During floods, river water levels show unpredictable fluctuations, introducing substantial noise that complicates accurate prediction. A hybrid model that uses eight-dimensional input data from hydrological and meteorological stations is proposed to address these challenges. Initially, the Variational Mode Decomposition preprocesses and denoises water level data, resulting in decomposed Intrinsic Mode Functions (IMFs). Then, the Pearson correlation coefficient between each IMF and input characteristics is computed, and the fluctuation factor for each IMF is defined. IMFs are categorized based on a threshold, leading to a hybrid prediction model. This model integrates convolutional neural networks (CNNs) for spatial information and bidirectional long short-term memory (BiLSTM) networks with an attention mechanism for learning from past and future data points. Comparative evaluations of mean absolute percentage error, root mean square error, mean absolute error, and goodness of fit (R2) show that the proposed model outperforms existing LSTM and CNN–BiLSTM frameworks, reducing RMSE by at least 20% and increasing R2 by approximately 10% on average. The model's practical significance lies in improving the accuracy and efficiency of meteorological forecasting and flood warning systems, contributing substantially to global disaster preparedness and response strategies.

Modelling of Riverine Flooding Using HEC-RAS-A Case Study of Badri Khawar River in KPK, Pakistan

Floods are one of the most occurring disasters in the world. Pakistan gets affected by flooding due to its diverse geography and has to lose several lives of people every year. The main objective of this study was to carry out modeling of riverine flooding using HEC-RAS program to estimate the safe channel capacity of the project reach and identify the vulnerable areas along the project reach for different flooding scenarios. A 9.21 Km long reach of Badri Khawar river, a right bank tributary of Indus River in KPK province has been taken as a case study. Based on the available data, one dimensional steady flow analysis in HEC-RAS has been carried out for different flooding scenarios. By analyzing the simulated results, the safe channel capacity of the study reach was found to be in the range of 125m3/s to 100m3/s from upstream to downstream. Based on the extent of flooding in different scenarios, vulnerable areas have been delineated on a flood map devised by the help of Google Earth. There is more inundation at the left overbank as compared to the right overbank. For the worst-case scenario, the maximum estimated flood extension on left bank side was about 425m while on the right bank side it was 204m. Based on the results of this research, it is suggested to enhance the existing flood protection measures along the project reach and improve the flood warning system and emergency evacuation mechanism in the vulnerable areas for long-term flood safety.

Keureuto Dam Operation for Flood Control System

Floods are one of the most destructive natural disasters. Dams are engineered structures built across rivers and streams to control water flow, primarily for purposes like water supply, hydroelectric power generation, and flood control. In the modern era, technological advancements and an enhanced understanding of hydrology have transformed dam operations within flood warning systems. Early warning systems for floods are founded on the principle of forecasting and alerting communities and authorities about imminent flood events. Keureuto Dam has multi-purpose benefits including to reduce flooding. The planned system includes sensors that record hydrological data (ARR and AWLR), equipment that receives the data recorded as well as a transmitter network from the sensors to the data center. in the Keureuto watershed, three AWLRs are planned to be installed upstream of the dam, one at the dam, seven AWLRs downstream of the dam. data from upstream is sent using a radio network, while from upstream it uses a cellular network. with a data center located at the dam.

From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh

River discharge forecasting stands at the forefront of environmental management, contributing significantly to sustainable development through its impact on flood prevention, water resource management, ecological conservation, and energy production. This study forecasted the annual river discharge forecasting in the Nilphamari district of Bangladesh, employing random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM) techniques. Historical river discharge data spanning from 1990 to 2020, obtained from eight surface water stations, forms the basis of the analysis. The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. Additionally, four evaluation plots, comprising a quantile–quantile plot (QQ plot), a residual plot, a Bland Altman plot, and Theil’s U statistic, were employed for a detailed understanding of model accuracy. Results demonstrate that the random forest regression technique exhibited superior accuracy compared to SVM and GBM in training and testing stages. Notably, the coefficient of determination reached 97 % during the testing phase, emphasizing the robustness of this model. While Mean Absolute Error is lower (1085.071 cubic meter per second), in training, the model captures relative changes (Mean Absolute Percentage Error = 0.154) better during prediction. Willmott’s Index in training (0.77) and testing (0.55) suggest the model memorizes training data well and outperforms the other models in testing stage. The findings underscore the efficacy of RF regression as a superior alternative for short-term discharge forecasting, offering valuable insights for integrated water resources management, particularly in flood warning systems and the expansion of irrigation initiatives.

An assessment of flash flood susceptibility in Golestan province, Iran, using multiple computational approaches

Purpose Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours. Design/methodology/approach This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed. Findings Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk. Originality/value The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

An empirical rainfall threshold approach for the civil protection flood warning system on the Milan urban area

Non-structural risk mitigation tools such as civil protection alerts for citizens proves highly beneficial in minimizing the impacts linked to floods. Flood forecasting represents a challenge due to complex and non-linear hydrological processes involved, especially in highly urbanized areas. In this study, a Flood Warning System (FWS) based on the development of catchment-specific empirical Rainfall Thresholds (RTs) is proposed.Seven river catchments in the “Hydraulic node of Milan,” northern Italy, were analyzed using a dataset of 25 years (1998–2022) of hourly rainfall and discharge data.An empirical methodology, based only on historical rainfall-runoff data and applicable to any river catchment, is proposed with the aim to validate and improve the existing Rainfall Threshold (RT) defined on the same area by the Lombardy Region civil protection.The RTs obtained using the proposed method showed improvements with respect to the existing civil protection RTs, because it allows to derive time-continuous and catchment-specific RTs. Additionally, accounting for the Antecedent Moisture Conditions (AMC) with the proposed “equivalent rainfall” approach results in more accurate RTs, suggesting its consideration for issuing civil protection alerts. The accuracy and uncertainty of the RTs were analyzed by means of binary classification measures coupled with bootstrap resampling.The proposed procedure for constructing RTs, which is applicable to any river catchment having sufficiently long time series of rainfall and runoff data, and not necessarily on urban areas only, indicates potential for being an additional and simple FWS to mitigate flood risks for civil protection purposes.