Impact of Urbanization on Pluvial Flooding: Insights from a Fast Growing Megacity, Dhaka

Sakib, Md Shadman; Alam, Siam; Shampa,; Murshed, Sonia Binte; Kirtunia, Ripan; Mondal, M. Shahjahan; Chowdhury, Ahmed Ishtiaque Amin

doi:10.3390/w15213834

Cited by 4 publications

(1 citation statement)

References 50 publications

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“…This situation is further aggravated by the alteration of natural waterways. In the pursuit of urban expansion and development, these waterways are often redirected or constrained, impacting their natural flow and capacity to handle floodwaters [37,38]. The result is a heightened risk of flooding, not just from the overburdened drainage systems but also from the altered waterways that can no longer accommodate sudden influxes of water.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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The consequences of climate change include extreme weather events, such as heavy rainfall. As a result, many places around the world are experiencing an increase in flood risk. The aim of this research was to assess the usefulness of selected machine learning models, including artificial neural networks (ANNs) and eXtreme Gradient Boosting (XGBoost) v2.0.3., for predicting peak stormwater levels in a small stream. The innovation of the research results from the combination of the specificity of small watersheds with machine learning techniques and the use of SHapley Additive exPlanations (SHAP) analysis, which enabled the identification of key factors, such as rainfall depth and meteorological data, significantly affect the accuracy of forecasts. The analysis showed the superiority of ANN models (R2 = 0.803–0.980, RMSE = 1.547–4.596) over XGBoost v2.0.3. (R2 = 0.796–0.951, RMSE = 2.304–4.872) in terms of forecasting effectiveness for the analyzed small stream. In addition, conducting the SHAP analysis allowed for the identification of the most crucial factors influencing forecast accuracy. The key parameters affecting the predictions included rainfall depth, stormwater level, and meteorological data such as air temperature and dew point temperature for the last day. Although the study focused on a specific stream, the methodology can be adapted for other watersheds. The results could significantly contribute to improving real-time flood warning systems, enabling local authorities and emergency management agencies to plan responses to flood threats more accurately and in a timelier manner. Additionally, the use of these models can help protect infrastructure such as roads and bridges by better predicting potential threats and enabling the implementation of appropriate preventive measures. Finally, these results can be used to inform local communities about flood risk and recommended precautions, thereby increasing awareness and preparedness for flash floods.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Starzec,

Kordana-Obuch

2024

Sustainability

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Assessing flooding extent and potential exposure to river pollution from urbanizing peripheral rivers within Greater Dhaka watershed

Khan,

Shawal,

Hossain

et al. 2024

Sci Rep

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Greater Dhaka area is home to large industrial clusters that are driving economic growth and the poverty reduction efforts of Bangladesh. These clusters are located around peripheral rivers- Turag, Buriganga, Dhaleswari, Balu, Shitalakhya, Bangshi, and Tongi-Khal, which are important for water transport, environment, and eco-systems where flooding of floodplains in monsoon is an integral part. The urban and industrial growth stressing natural resources has led to severe degradation of the rivers and floodplains, affecting the livelihoods, health, and well-being of the people. Monsoon-time exposure to polluted water is yet to be studied and addressed scientifically. This study looked into the water quality and flooding situation of Greater Dhaka for two successive monsoons through extensive river sampling coupled with the estimation of flooded area and exposed population using remote sensing tools. Sentinel 1’s Synthetic Aperture Radar (SAR) images are used for flood mapping considering cost-effectiveness and its advantages for data-scarce regions. The estimated population exposed to flooding was over 668,000 in 2019, and this number increased by 1.53 times in 2020, totaling over one million. During the monsoon and post-monsoon periods of 2019 and 2020, Buriganga, Tongi Khal, and Balu were consistently in poor condition. The lowest water quality index (WQI) was observed in Balu during the monsoon of 2019 (32.28), post-monsoon of 2019 (35.71), and post-monsoon of 2020 (29.58). The lowest WQI during the monsoon of 2020 was recorded in Tongi Khal (35.75). Among the four districts, Dhaka and Gazipur were the most affected by floods in terms of inundation area and exposed population. Our study indicates that most rivers remain in poor condition during the monsoon when exposure is also high. This highlights the need for policymakers to take monsoon exposure seriously and design appropriate interventions. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-80063-4.

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Spatial and Temporal Variability in Bioswale Infiltration Rate Observed during Full-Scale Infiltration Tests: Case Study in Riga Latvia

Kondratenko,

Boogaard,

Rubulis

et al. 2024

Water

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Urban nature-based solutions (NBSs) are widely implemented to collect, store, and infiltrate stormwater. This study addressed infiltration rate as a measure of the performance of bioretention solutions. Quick scan research was conducted, starting with mapping over 25 locations of implemented green infrastructure in Riga, Latvia. Basic information, such as location, characteristics, as well as photos and videos, has been uploaded to the open-source database ClimateScan. From this, eight bioswales installed in the period 2017–2022 were selected for hydraulic testing, measuring the infiltration capacity of bio-retention solutions. The results show a high temporal and spatial variation of infiltration rate for the bioswales, even those developed with similar designs: 0.1 to 7.7 m/d, mean 2.0 m/d, coefficient of variation 1.0. The infiltration capacity decreased after saturation: a 30% to 58% decrease in infiltration rate after refilling storage volume. The variation in infiltration rate as well as infiltration rate decrease on saturation is similar to other full-scale studies done internationally. The infiltration rate of most bioswales falls within the range specified by international guidelines, all swales empty within 48 h. Most bioswales empty several times within one day, questioning the effectiveness of water retention and water availability for dry periods. The results are of importance for stakeholders involved in the implementation of NBS and will be used to set up Latvian guidelines for design, construction, and maintenance.

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Impact of Urbanization on Pluvial Flooding: Insights from a Fast Growing Megacity, Dhaka

Cited by 4 publications

References 50 publications

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Evaluating the Utility of Selected Machine Learning Models for Predicting Stormwater Levels in Small Streams

Assessing flooding extent and potential exposure to river pollution from urbanizing peripheral rivers within Greater Dhaka watershed

Spatial and Temporal Variability in Bioswale Infiltration Rate Observed during Full-Scale Infiltration Tests: Case Study in Riga Latvia

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