| id |
caadria2024_88 |
| authors |
Li, Jiongye and Stouffs, Rudi |
| year |
2024 |
| title |
Convolutional Neural Network-Based Predictions of Potential Flash Flood Hotspots in Singapore: Insights and Strategic Interventions |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 69–78 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.069
|
| summary |
Amid increasing urbanization, changing climate, and limited stormwater infrastructure, urban flooding is a global issue, and Singapore is no exception. Traditional identification of flood-prone areas in Singapore has relied on historical flash flood data. However, by applying the booming influx of big data across various domains, including geography, weather, and DEM data, and using the deep learning model, Convolutional Neural Network (CNN), this research proposes a method that can accurately and effectively predict flash flood spots in an urban environment. Specifically, datasets including elevation, slope, aspect, rainfall, canals, drainage, and land use are fed into the CNN model to predict the locations of flash floods. The model, with a testing accuracy of 0.962, generates a comprehensive flash flood assessment map identifying high-risk areas in Singapore. Contrary to the current flood-prone area identification, which classifies only 0.79% of the country as susceptible to flash floods based on historical events, our CNN model-based assessment indicates that 11.4% of the country is at high risk. These newly identified zones are predominantly located along the coastline and in low-lying watershed outlets. Additionally, we propose corresponding stormwater infrastructure enhancements to mitigate flash flooding in these locations. |
| keywords |
flash floods, flood prediction, convolutional neural network, geospatial data, flash flood assessment map, stormwater management measures |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (773,714 bytes) |
| references |
Content-type: text/plain
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Bai, Z., & Peng, C. (2023)
 Convolutional Neural Network (CNN) Supported Urban Design to Reduce Particle Air Pollutant Concentrations
, HUMAN-CENTRIC, Proceedings of the 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2023, Volume 1, 505-514
|
|
|
|
|
Chang, C. L., Lo, S. L., & Huang, S. M. (2008)
Optimal strategies for best management practice placement in a synthetic watershed
, Environmental monitoring and assessment, 2009-06, Vol.153 (1-4), p.359-364"
|
|
|
|
|
Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019)
An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
, The Science of the total environment, 2019-02, Vol.651 (Pt 2), p.2087-2096"
|
|
|
|
|
Chow, W. T. L., Cheong, B. D., & Ho, B. H. (2016)
A Multimethod Approach towards Assessing Urban Flood Patterns and Its Associated Vulnerabilities in Singapore
, Advances in Meteorology, 2016, 1-11
|
|
|
|
|
Fletcher, T. D., Shuster, W., Hunt, W. F., Ashley, R., Butler, D., Arthur, S., Trowsdale, S., Barraud, S., Semadeni-Davies, A., Bertrand-Krajewski, J.-L., Mikkelsen, P. S., Rivard, G., Uhl, M., Dagenais, D., & Viklander, M. (2014)
SUDS, LID, BMPs, WSUD and more - The evolution and application of terminology surrounding urban drainage
, Urban water journal, 2015-10, Vol.12 (7), p.525-542"
|
|
|
|
|
Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S., Tiede, D., & Aryal, J. (2019)
Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection
, Remote sensing (Basel, Switzerland), 2019-01, Vol.11 (2), p.196, Article 196
|
|
|
|
|
Khosravi, K., Shahabi, H., Pham, B. T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G., Ho, H. L., Hong, H., Chapi, K., & Prakash, I. (2019)
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
, Journal of hydrology (Amsterdam), 2019-06, Vol.573, p.311-323
|
|
|
|
|
Li, C., Peng, C., Chiang, P.-C., Cai, Y., Wang, X., & Yang, Z. (2019)
Mechanisms and applications of green infrastructure practices for stormwater control: A review
, Journal of hydrology (Amsterdam), 2019-01, Vol.568, p.626-637
|
|
|
|
|
Sameen, M. I., Pradhan, B., & Lee, S. (2020)
Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
, Catena (Giessen), 2020-03, Vol.186, p.104249, Article 104249
|
|
|
|
|
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014)
Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
, Journal of hydrology (Amsterdam), 2014-05, Vol.512, p.332-343
|
|
|
|
|
Wang, X., Kinsland, G., Poudel, D., & Fenech, A. (2019)
Urban flood prediction under heavy precipitation
, Journal of hydrology (Amsterdam), 2019-10, Vol.577, p.123984, Article 123984
|
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| last changed |
2024/11/17 22:05 |
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