Development and Application of an Urban Flood Forecasting and Warning Process to Reduce Urban Flood Damage: A Case Study of Dorim River Basin, Seoul

Early and accurate flood forecasting and warning for urban flood risk areas is an essential factor to reduce flood damage. This paper presents the urban flood forecasting and warning process to reduce damage in the main flood risk area of South Korea. This process is developed based on the rainfall-runoff model and deep learning model. A model-driven method was devised to construct the accurate physical model with combined inland-river and flood control facilities, such as pump stations and underground storages. To calibrate the rainfall-runoff model, data of gauging stations and pump stations of an urban stream in August 2020 were used, and the model result was presented as an R2 value of 0.63~0.79. Accurate flood warning criteria of the urban stream were analyzed according to the various rainfall scenarios from the model-driven method. As flood forecasting and warning in the urban stream, deep learning models, vanilla ANN, Long Short-Term Memory (LSTM), Stack-LSTM, and Bidirectional LSTM were constructed. Deep learning models using 10-min hydrological time-series data from gauging stations were trained to warn of expected flood risks based on the water level in the urban stream. A forecasting and warning method that applied the bidirectional LSTM showed an R2 value of 0.9 for the water level forecast with 30 min lead time, indicating the possibility of effective flood forecasting and warning. This case study aims to contribute to the reduction of casualties and flood damage in urban streams and accurate flood warnings in typical urban flood risk areas of South Korea. The developed urban flood forecasting and warning process can be applied effectively as a non-structural measure to mitigate urban flood damage and can be extended considering watershed characteristics.

Process-based flood damage modelling relying on expert knowledge: a methodological contribution applied to agricultural sector

Flood damage assessment is crucial for evaluating flood management policies. In particular, properly assessing damage to the agricultural assets is important because they may have greater exposure and are complex economic systems. The modelling approaches used to assess flood damage are of several types and can be fed by damage data collected post-flood, from experiments or based on expert knowledge. The process-based models fed by expert knowledge are subject of research and also widely used in an operational way. Although identified as potentially transferable, they are in reality often case-specific and difficult to reuse in time (updatbililty) and space (transferability). In this paper, we argue that process-based models are not doomed to be context specific as far as the modelling process is rigorous. We propose a methodological framework aiming at verifying the conditions necessary to develop these models in a spirit of capitalisation by relying on four axes which are: i/ the explicitation of assumptions, ii/ the validation, iii/ the updatability, iv/ the transferability. The methodological framework is then applied to the model we have developed in France to produce national damage functions for the agricultural sector. We show in this paper that the proposed methodological framework allows an explicit description of the modelling assumptions and data used, which is necessary to consider a reuse in time or a transfer to another geographical area. We also highlight that despite the lack of feedback data on post-flood damages, the proposed methodological framework is a solid basis to consider the validation, transfer, comparison and capitalisation of data collected around process-based models relying on expert knowledge. In conclusion, we identify research tracks to be implemented to pursue this improvement in a spirit of capitalisation and international cooperation.

Development of an ANN-Based Urban Flood Alert Criteria Prediction Model and the Impact of Training Data Augmentation

Urban flooding occurs during heavy rains of short duration, so quick and accurate warnings of the danger of inundation are required. Previous research proposed methods to estimate statistics-based urban flood alert criteria based on flood damage records and rainfall data, and developed a Neuro-Fuzzy model for predicting appropriate flood alert criteria. A variety of artificial intelligence algorithms have been applied to the prediction of the urban flood alert criteria, and their usage and predictive precision have been enhanced with the recent development of artificial intelligence. Therefore, this study predicted flood alert criteria and analyzed the effect of applying the technique to augmentation training data using the Artificial Neural Network (ANN) algorithm. The predictive performance of the ANN model was RMSE 3.39-9.80 mm, and the model performance with the extension of training data was RMSE 1.08-6.88 mm, indicating that performance was improved by 29.8-82.6%.

Quantitative Flood Damage Evaluation Using Grid-Based Spatial Analysis Data

In Korea, flood damage occurs every year due to typhoons and heavy rains, resulting in increasing damage to human lives and properties in urban areas. To reduce the scale of flood damage, economic analyses of flood-control work are conducted as part of efficient disaster management in the context of a limited budget. In this study, a quantitative evaluation of flood damage in Ulsan due to Typhoon Chaba was conducted using multi-dimensional flood damage analysis (MD-FDA). However, the land cover map applied to MD-FDA has limited data resolution and update intervals. Examination of domestic and foreign research cases to complement these spatial analysis data showed that grid data were being used in disaster-related fields. This study evaluated whether grid data are suitable for quantitative assessment through economic analyses conducted using new spatial analysis data such as road name address digital maps and 100 × 100 m grid-based spatial analysis data. The results of this study confirm that center-point-method grid data constitute spatial analysis data suitable for economic analysis.

Drone-Based Water Level Detection in Flood Disasters

Japan was hit by typhoon Hagibis, which came with torrential rains submerging almost eight-thousand buildings. For fast alleviation of and recovery from flood damage, a quick, broad, and accurate assessment of the damage situation is required. Image analysis provides a much more feasible alternative than on-site sensors due to their installation and maintenance costs. Nevertheless, most state-of-art research relies on only ground-level images that are inevitably limited in their field of vision. This paper presents a water level detection system based on aerial drone-based image recognition. The system applies the R-CNN learning model together with a novel labeling method on the reference objects, including houses and cars. The proposed system tackles the challenges of the limited and wild data set of flood images from the top view with data augmentation and transfer-learning overlaying Mask R-CNN for the object recognition model. Additionally, the VGG16 network is employed for water level detection purposes. We evaluated the proposed system on realistic images captured at disaster time. Preliminary results show that the system can achieve a detection accuracy of submerged objects of 73.42% with as low as only 21.43 cm error in estimating the water level.

INSYDE-BE: Adaptation of the INSYDE model to the Walloon Region (Belgium)

The spatial transfer of flood damage models among regions and countries is a challenging but unavoidable approach, for performing flood risk assessments in data and model scarce regions. In these cases, similarities and differences between the contexts of application should be considered to obtain reliable damage estimations and, in some cases, the adaptation of the original model to the new conditions is required. This study exemplifies a replicable procedure for the adaptation to the Belgian context of a multi-variable, synthetic flood damage model for the residential sector originally developed for Italy (INSYDE). The study illustrates necessary amendments in model assumptions, especially regarding input default values for the hazard and building parameters and damage functions describing the modelled damage mechanisms.

Identification of Flood-prone Areas using HEC-HMS and HEC-RAS, the Case of Ciberang River Basin, Lebak District of Banten Province

It is common practice that flood hydrograph simulations help to provide better flood prediction and flood damage reduction planning. These efforts require information on flood-prone areas identification from the hydrological and hydraulic analysis results. Historically, the Ciberang River Basin has experienced floods. Those floods cause the loss of human life and damage some houses along the river’s channels, especially in Lebak District, Banten Province, Indonesia. The main objective of this study is to identify flood-prone areas based on the simulation result of a hydrologic and hydraulic model of catchment response due to several extreme rainfall events using HEC-HMS and HEC-RAS software. Rainfall and discharge data measured at the Ciberang-Sabagi water level gauge on 10 January 2013 were used to calibrate hydrological watershed parameters. The hydraulics channel routing is started from the planned location of the Sabo dam to the downstream control point. The next stage was the simulation of rainfall-runoff transformation and 1D unsteady flow channel routing for the 2, 5, and 10-years floods return periods. The main result of this study is a flood hazards map that shows the spatial distribution of the area and inundation depth for each return period of the flood.

A Novel Framework for Urban Flood Damage Assessment

The study of non-stationary effects of hydrological time series and land-use changes in urban areas is essential to predict future floods and their probable damage. In the current study, a novel method was proposed for analyzing their simultaneous impact. For this purpose, rainfall frequency and land-use changes analyses were conducted for two different long-term periods, and the results were compared. Then, hydrologic modeling of the catchment was carried out using the HEC-HMS model, and obtained hydrographs were fed to the HEC-RAS2D model for estimating flood inundation areas. Using the economic information of assets and their damage functions, flood damages related to these two periods were evaluated through the HEC-FIA model. The results indicated that in the low return periods (e.g., 2-year flood), the damage in the second period was increased with respect to the first one but increased for the return periods of 5 to 100 years. Furthermore, surface runoff showed a 4.65% increase due to land-use change and a 12% increase due to rainfall non-stationarity. Moreover, flood damage showed a 136% increase on average, and among the two studied factors, the non-stationarity of rainfalls is considerably more effective on flood intensification.

Moving forward: Future proofing stilt home interiors while incorporating cultural identity

<p>Kampong Cham is a province in Cambodia that is prone to annual flooding. This affects many families and causes damage and displacement of households every year. This research aims to offer a design solution that enables families to prepare for, endure and mitigate damage during the rainy season while incorporating traditional and cultural features. A site visit was undertaken to allow for qualitative and quantitative data to be gathered, as was ongoing investigation into Cambodia’s traditions and culture in order to understand what is important to the families in this province. An iterative design process was pursued and a final design concept that seeks solutions and has considered the many issues is presented. The design endeavours to mitigate flood damage and provide a living environment to which families can relate. In addition, it aims to improve the living standards within Cambodia through cost-effective design and materials.</p>