Flood Research Articles

Rainfall Prediction using Backpropagation Method and GIS for Disaster Mitigation Mapping

Frequent weather or climate changes, coupled with the topography of Cirebon and Majalengka which are mostly lowlands and close to the sea, raise concerns that flood disasters can occur if mitigation is not done early. This research aims to predict and map rainfall and runoff discharge as a flood mitigation effort in the Cirebon and Majalengka areas. The method used in this research is the Backpropagation Method, which resembles the way the neuron system works in the human brain to learn patterns, optimize weights and biases, and reduce the error rate (mean square error) so that the predicted value is closer to the actual value. The results showed that Cirebon was predicted to have the highest rainfall of 2471 mm in 2048, based on prediction data from BMKG Penggung Station, while Majalengka was predicted to have the highest rainfall of 2922 mm in 2048, based on prediction data from BMKG Kertajati Station. The largest runoff discharge in the Cirebon region occurred in Susukan District (51.96 km²) with a discharge of 68.648 m³/second (Q25), and in the Majalengka region occurred in Lemahsugih District (78.64 km²) with a discharge of 104.16 m³/second (Q25). The implications of this research include mitigation efforts that can be carried out, such as reducing development above drainage channels, reforestation, maintaining open areas, reducing land use change, conservation of rivers and catchments, construction of flood embankments, and issuance of related regulations.

Feasibility of Emergency Flood Traffic Road Damage Assessment by Integrating Remote Sensing Images and Social Media Information

In the context of global climate change, the frequency of sudden natural disasters is increasing. Assessing traffic road damage post-disaster is crucial for emergency decision-making and disaster management. Traditional ground observation methods for evaluating traffic road damage are limited by the timeliness and coverage of data updates. Relying solely on these methods does not adequately support rapid assessment and emergency management during extreme natural disasters. Social media, a major source of big data, can effectively address these limitations by providing more timely and comprehensive disaster information. Motivated by this, we utilized multi-source heterogeneous data to assess the damage to traffic roads under extreme conditions and established a new framework for evaluating traffic roads in cities prone to flood disasters caused by rainstorms. The approach involves several steps: First, the surface area affected by precipitation is extracted using a threshold method constrained by confidence intervals derived from microwave remote sensing images. Second, disaster information is collected from the Sina Weibo platform, where social media information is screened and cleaned. A quantification table for road traffic loss assessment was defined, and a social media disaster information classification model combining text convolutional neural networks and attention mechanisms (TextCNN-Attention disaster information classification) was proposed. Finally, traffic road information on social media is matched with basic geographic data, the classification of traffic road disaster risk levels is visualized, and the assessment of traffic road disaster levels is completed based on multi-source heterogeneous data. Using the “7.20” rainstorm event in Henan Province as an example, this research categorizes the disaster’s impact on traffic roads into five levels—particularly severe, severe, moderate, mild, and minimal—as derived from remote sensing image monitoring and social media information analysis. The evaluation framework for flood disaster traffic roads based on multi-source heterogeneous data provides important data support and methodological support for enhancing disaster management capabilities and systems.

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

An event logic graph for geographic environment observation planning in disaster chain monitoring

Effective geographic environment observation planning is the key to obtain disaster monitoring and warning information. The previous researches can only make observation plans for a single disaster at some specific stages. They are difficult to apply to the dynamic evolution of the disaster chain. Timely and comprehensive geographic environment observation planning is urgently needed to provide high-value monitoring data for the identification and response of secondary disaster chains. Event logic graph (ELG) shows great potential in evolutionary law expression and chain event reasoning. Therefore, this study proposed an observation ELG (OELG), in which events and their logical relationships are modeled as nodes and edges to express the occurrence and development motivation of observation events. The disaster chain observation planning can be transformed into the reasoning of potential continuous observation events. Subsequently, an OELG-based geographic environment observation planning framework was proposed, which realizes the construction, instantiation, and plan reasoning of OELG. The observation planning experiment was carried out taking the flood disaster chain that occurred in Beijing, China and Nordrhein-Westfalen, Germany as examples. The results show that OELG can generate disaster chain observation plan more timely, comprehensively, and continuously than other models, thus providing support for disaster chain risk monitoring and emergency response.

Accurate simulation of extreme rainfall–flood events via an improved distributed hydrological model

Recently, the high incidence of extreme rainfall and flood events worldwide has severely harmed regional economies and societies. Therefore, flood simulations and forecasts, which can provide key technical support for regional flood control and disaster reduction, are urgently needed. The Liuxihe model, as a fully distributed, physically based hydrological model, was improved in this study to simulate extreme rainfall flood events in the Beijiang River Basin. This basin is a famous rainstorm centre in Guangxi Province, China. In this work, the Liuxihe model is improved in two aspects: first, its structure and runoff generation and confluence algorithm are improved, and second, the parameter calibration method is optimised. These two adjustments improve the flood simulation performance of the model and reduce the uncertainty of the simulation results. The results revealed that the flood simulated by the improved Liuxihe model was strongly consistent with the measured values, and the index values of the Nash coefficient, correlation coefficient, process relative error, and flood peak flow error performed very well in the scheme evaluation; in particular, the mean process relative error, flood peak error, and peak time difference decreased by 62%, 63%, and 80%, respectively, after model improvement. The error indicators of the simulation were within the allowable error range from the Standard for Hydrological Information and Hydrological Forecasting (GB/T-22482–2008), which meets the accuracy requirements of flood forecasting in the local hydrological department and can be used as a practical operational plan for flood forecasting. These satisfactory flood simulation results showed that the model and algorithm were improved; thus, the improved Liuxihe model can provide important theoretical guidance for regional flood forecasting and flood disaster mitigation.

Hydro-informer: a deep learning model for accurate water level and flood predictions

AbstractThis study aims to develop an advanced deep learning model, Hydro-Informer, for accurate water level and flood predictions, emphasizing extreme event forecasting. Utilizing a comprehensive dataset from the Slovak Hydrometeorological Institute SHMI (2008–2020), which includes precipitation, water level, and discharge data, the model was trained using a ladder technique with a custom loss function to enhance focus on extreme values. The architecture integrates Recurrent and Convolutional Neural Networks (RNN, CNN), and Multi-Head Attention layers. Hydro-Informer achieved significant performance, with a Coefficient of Determination (R2) of 0.88, effectively predicting extreme water levels 12 h in advance in a river environment free from human regulation and structures. The model’s strong performance in identifying extreme events highlights its potential for enhancing flood management and disaster preparedness. By integrating with diverse data sources, the model can be used to develop a well-functioning warning system to mitigate flood impacts. This work proposes a novel architecture suitable for locations without water regulation structures.

Design and Development of a Prototype Water Level Control System for Early Flood Detection Based on The Internet of Things: a Case Study of Kambaniru Dam

Flooding can occur due to overflowing water during the rainy season, therefore early detection of the water level in dams as water runoff processing structures is needed. When heavy rain occurs it can result in flood disasters that are detrimental to communities, dam infrastructure and agricultural land. This research aims to monitor water levels online as initial information about impending flood disasters. Monitoring using Internet of Things (IoT) based technology is intended to obtain water level information in real time. In this device the HC-SR04 ultrasonic sensor is used as a water level reader, a waterflow sensor as a water discharge reader and a servo motor as a sluice gate controller. The results of this research are a prototype of a water level detection device that can provide water level information and can control sluice gates. automatically and can be controlled manually or remotely via the latest notifications in the Blynk application using the MQTT protocol. This research was carried out by taking information based on the results of direct observations and interviews with officers at Kambaniru Dam, Lambanapu Village, Kambera District. In this way, the prototype of this detection tool will be easy to use as initial information about the possibility of flooding.

The role of flood disaster information literacy in community preparedness in Tempe district Wajo regency South Sulawesi province Indonesia

The coast of Lake Tempe is one of the areas in Indonesia which is inhabited by approximately 500 people. Every year, the water of Tempe Lake rises and overflows, a form of vigilance for the surrounding community. This study aims to determine the Tempe community's level of information literacy towards floods and the role of disaster information literacy in community preparedness for floods. The method used in this research is descriptive qualitative. The results of this study indicate that disaster literacy is obtained through activities carried out by the government and mitigation carried out by the government, which can also be interpreted as taming natural disasters. In principle, Mitigation is physical and non-physical efforts in dealing with natural disasters. However, the people around Lake Tempe are not afraid and anxious about flooding and overflowing in Lake Tempe. It can be seen that the flood was not a disaster for the people in Lake Tempe. The role of disaster information literacy can be seen in the community's readiness to welcome floods by providing iron, wooden planks, and bamboo to increase the height and strengthen the community's houses while in the Tempe sub-district. Socialization and education about disaster literacy is an effort that needs to be made by the government to continue to change the mindset of the people of Wajo Regency, who think that flooding is not a disaster.

Dynamics System Modeling of Declining Agricultural Land Area Potentially Causing Jakarta Flood Disaster

The change or evolution of land use in the DKI Jakarta area has caused various environmental problems in the city that have an impact on disasters that occur from time to time. The conversion of agricultural land that has a relatively high ability to absorb water into built-up land has reduced the water absorption capacity because the built-up area is increasingly impermeable which is at risk of causing flood disasters. This research focuses on a simple dynamic system modeling approach to understand the cause-and-effect relationship of the decline in agricultural land area in the DKI Jakarta area. The method used in this research is a qualitative-based dynamic system, dynamic system is a method that can be used to form structures and predict behavior and feedback. Dynamic system modeling using Powersim Studi 10 Express software uses time specifications ranging from 2020 to 2070. The results of the dynamic system model of agricultural land decline can be said to be valid, where the dominant structure formed is Negative Feedback (Balancing Loop) with Exponential Decay behavior and Average Means Error (AME) test results obtained a value of 29.6%

Resilience evaluation of multi-feature system based on hidden Markov model

Modern systems have become increasingly vulnerable to threats due to their growing complexity nowadays. Multi-feature systems, prevalent in the realm of complex structures, manifest their performance through a diverse array of features. In response to threats, this paper develops a resilience evaluation model for multi-feature systems based on hidden Markov models, which can describe the dynamic relationship between performance levels and external features. Quantitative resilience indicators are presented across three distinct dimensions: resistant, absorption, and recovery, whose analytical formulas are derived by generating functions and properties are proved. Meanwhile, simulation algorithms are proposed to verify the correctness of the analytic formulas. Finally, taking the system under the threat of flood disasters as an example, the resilience model proposed in this paper is applied to evaluate its resilience, and the robustness of the resilience evaluation indicators is verified.

Integrated urban and riverine flood risk management in the Fujiang River Basin-Mianyang city

China’s monsoon climate, characterized by hot summers and substantial rainfall, leads to significant seasonal water accumulation in river basins. This often results in short-term heavy rainfall events, causing rapid water flow into river basins and subsequent flooding. River embankments, which narrow channels, exacerbate downstream flooding and pose severe safety threats. This study explores integrated urban and riverine flood risk management strategies within the Fujiang River Basin, focusing on Mianyang City. The research addresses the growing challenges of global climate change and rapid urbanization, which have intensified flood risks. Utilizing the one-dimensional Storm Water Management Model (SWMM) and two-dimensional hydrodynamic Rainfall-Runoff-Inundation (RRI) model, the study simulates flood events and urban flooding in Mianyang City and the Fujiang River Basin. These models were validated with data from hydrological stations and satellite imagery. The study evaluates extreme flood events in Mianyang City between 2015 and 2021. The findings highlight the significant role of sponge city initiatives and low-impact development (LID) facilities in mitigating flood impacts. Despite their benefits, these measures have limitations under extreme flood conditions. This study contributes to understanding the interactions between urban and riverine systems and provides a basis for improving flood risk management and resilience in rapidly urbanizing river basins. It supports ongoing efforts to implement sponge city concepts in China, offering crucial insights into effective strategies for managing flood risks in similar geographic and climatic contexts.

Integrating Intelligent Hydro-informatics into an effective Early Warning System for risk-informed urban flood management

The urban drainage system constantly facing flooding issues in coastal and urban areas. Robust and accurate urban flood management, particularly considering fast-moving compound floods, is crucial to minimize the impact of flood disasters in coastal cities. Till now, Ho Chi Minh City (HCMC) lacks an effective means of urban flood management because of flood risk communication among residents. Existing flood risk communication tools rely on post-disaster flood model outcomes and data. Therefore, this research proposes a real-time Early Urban Flooding Warning System (EUFWS) integrated with a user-friendly web and app interface. The backbone of this system consists of flood models developed using machine learning (ML) algorithms, combined with big data and Web-GIS visualization, with ML serving as the core for constructing the EUFWS. EUFWS offer several key advantages: they are available at all times, accessible from anywhere, and provide a real-time, multi-user working platform. Additionally, the system is flexible, allowing for the easy addition of components and services and scalable, adjusting to workload demands. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. EUFWS have been successfully deployed in Thu Duc City, Vietnam, as a case study and are operating effectively. Research results indicate that EUFWS supported decision-makers to be effectively risk informed and make intelligent decisions during urban flood emergencies. This underscores the significant potential of integrating ML and information technology to enhance the management of smart urban drainage systems in flood-prone cities worldwide.

Whether higher risk indicates severe loss in the flood disaster assessment: A comparative analysis in the Hubei province of central China

Study regionHubei province, Yangtze River Basin, Central China Study focusRisk and loss analysis constitutes a critical element of flood disaster management. Nevertheless, the relationship between risk and loss has been infrequently examined in the literature. Here we developed a comparative analysis framework for assessing flood risk and loss in Hubei Province. The risk model incorporates 22 indicators in terms of hazard, exposure, vulnerability and emergency response & recovery capability. Indicator weights were determined through a combination of objective and subjective weighting methods based on the game theory. Flood risk and loss levels were classified using fuzzy comprehensive evaluation and grey correlation analysis respectively. New hydrological insightsAnnual risk and loss maps exhibit a spatial pattern of higher levels in the eastern regions and lower levels in the western regions. However, the relationship between risk and loss was not as well as expected during designated flood periods. While regions impacted by disasters typically demonstrated higher risk levels, the risk in the regions unaffected by flood disasters appeared to be overestimated. By analyzing flood risk and loss across various temporal scales, this research contributes a novel perspective for the evaluation of flood risk models.

Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development.

An Application of Hybrid Bagging-Boosting Decision Trees Ensemble Model for Riverine Flood Susceptibility Mapping and Regional Risk Delineation

An Application of Hybrid Bagging-Boosting Decision Trees Ensemble Model for Riverine Flood Susceptibility Mapping and Regional Risk Delineation

Opportunities and barriers for skillful sub-seasonal prediction of East Asian summer precipitation

Abstract Accurate sub-seasonal (2-8 weeks) prediction of monsoon precipitation is crucial for mitigating flood and heatwave disasters caused by intra-seasonal variability (ISV). However, current state-of-the-art sub-seasonal-to-seasonal (S2S) models have limited prediction skills beyond one week when predicting weekly precipitation. Our findings suggest that predictability primarily arises from strong ISV events, and the prediction skills for ISV events depend on the propagation stability of preceding signals, regardless of models. This allows us to identify opportunities and barriers (OBs) within S2S models, clarifying what the models can and cannot achieve in ISV event prediction. Focusing on the complex East Asian summer monsoon (EASM), we discover that stable propagation of Eurasian and tropical atmospheric wave trains towards East Asia serves as an opportunity. This opportunity offers a one-week leading prediction skill of up to 0.85 and skillful prediction up to 13 days ahead for 43% of all ISV events. However, the Tibetan Plateau barrier highlights the limitation of EASM predictability. Identifying these OBs will help us gain confidence in making more accurate sub-seasonal prediction.

Urban Planning Perspective on Food Resilience Assessment and Practice in the Zhengzhou Metropolitan Area, China

This study aims to assess and analyze the urban food resilience of the Zhengzhou metropolitan area, proposing innovative assessment frameworks and methodologies. Utilizing a dual-level analysis approach that combines long-term planning impact analysis (2000–2020) with short-term resilience assessment (2018–2022), the study integrates public government data and Geographic Information System (GIS) data, employing spatial analysis, Analytic Hierarchy Process (AHP), and fuzzy comprehensive evaluation techniques. Findings from 2000 to 2020 indicate that urban planning within the metropolitan area has significantly impacted the food system. Urbanization has led to reduced agricultural land, but improvements in infrastructure have enhanced the efficiency of the food supply chain. Woodland and grassland areas have remained relatively stable, providing an ecological buffer for the food system. Building on this, the short-term assessment from 2018 to 2022 reveals significant dynamic changes and a continuous improvement trend in food resilience, though there is still room for enhancement. Food supply chain management and emergency preparedness and management contributed the most to overall resilience. Notably, extreme events such as the COVID-19 pandemic and the “7.20 Flood Disaster” prompted the adoption of innovative measures to enhance food resilience. The study develops a multidimensional theoretical framework and assessment system for urban food resilience, offering new perspectives and methods for understanding and enhancing urban food resilience. The results highlight the critical role of urban planning in enhancing food resilience, recommending the integration of the food system into comprehensive urban planning, strengthening regional collaboration, and enhancing public engagement. These findings provide an important basis for policymaking and practice aimed at improving the long-term adaptability and short-term recovery capabilities of urban food systems.

Musculoskeletal Health Problems Among the Loom Weavers of Sirajganj District, Bangladesh

Land use change is increasing in West Nusa Tenggara province, especially in Bima district. This condition occurs because people who make land use change patterns that are utilized as agricultural areas for corn commodities. The limited agricultural land in Bima Regency with the potential for good selling prices of corn commodities is a major factor in land use change, this has an impact on flood disaster vulnerability. This study aimed to determine the condition of the livelihood assets of seasonal corn farmers in the Bima district. The research method used a descriptive presentative statistical approach. The sample in this study amounted to 97 respondents who were taken in 8 districts with flood vulnerability, namely Sanggar, Madapangga, Palibelo, Woha, Belo, Monta, Sape, and Lambu. Random sampling was used to determine the respondents. The results showed that the condition of the livelihood assets of seasonal corn farmers in Bima District from 8 flood-prone villages is dominated by human resources, where the average percentage value is 83%. This is followed by physical assets at 81% and natural and financial resources at 71%, while the lowest is social assets with a percentage value of 69%. The farmer livelihood assets with high percentage values in each flood-prone area are human resources in Sanggar, natural resources in Palibelo, financial and physical in Sape, and social assets in Palibelo and Woha. The pentagon of livelihood assets does not form a triangle, so it can be concluded that there is inequality between assets. The lack of agricultural land owned by farmers encourages the use of land cover as corn farmland. This can be seen from the use of land by farmers that is not their own, resulting in environmental damage that causes flood vulnerability. Keywords: Land use change, Livelihood assets, Flood Disaster

Model Komunikasi dalam Mitigasi Bencana Banjir di Kabupaten Kerinci dan Sungai Penuh

This article discusses the problems of development communication in flood disaster mitigation in Kerinci Regency and Sungai Penuh City. Kerinci Regency with its mountainous topography and extensive river network often experiences flood disasters, which have worsened in recent years. This study aims to explore an effective communication model in reducing the impact of flooding and improving community preparedness. Using a literature study method, this study highlights the importance of two-way communication between the government and the community, community-based education, and the use of modern technology such as social media and mobile applications. The results of the study indicate that a participatory communication model that actively involves the community and a technology-based early warning system can improve disaster mitigation and community response. This article provides a more inclusive communication approach, involving various stakeholders, to strengthen community resilience to future disasters.

Implementasi Penyaluran Bantuan Sosial Kepada Korban Bencana Banjir di Kecamatan Waru Kabupaten Sidoarjo

This research aims to describe in more detail the distribution of social assistance (bansos) carried out by the Sidoarjo Regency Government to residents of Waru District who were affected by the flood disaster. Using qualitative research methods, this research refers to David C. Korten's Implementation Theory with three indicators, namely 1) Conformity between the program and utilization, 2) Conformity between the program and the implementing organization, and 3) Conformity between the user group and the implementing organization. This research uses a three-stage data collection technique, namely interviews, documentation, and literature study involving the Sidoarjo Regency Government, stakeholders involved, and residents of Waru District who were affected by the flood. The research results show that the distribution of social assistance for flood disasters in Waru District has operated well, with indicators 1) The suitability of the social assistance program which has been right on target, namely distributed to flood disaster victims in Waru District; 2) Suitability of the program that has been implemented by the appropriate officer according to their field; and 3) Suitability for the people of Waru District who receive social assistance from the Sidoarjo Regency Government in a structured and coordinated manner.