Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka

Wagenaar, Dennis; Dahm, Ruben; Diermanse, Ferdinand; Dias, W. P. S.; Dissanayake, D.M.S.S.; Vajja, Hari Prasad; Gehrels, J.C.; Bouwer, Laurens M.

doi:10.1016/j.ijdrr.2019.101162

Cited by 31 publications

(15 citation statements)

References 17 publications

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“…In this regard, ML assists urban growth and infrastructure development projection to better recognize areas in the high risk of flood due to high exposure. ML models can also help identify the potential land‐use change in the future that requires revisiting flood mitigation measures (Hosseini et al., 2020; Jaad & Abdelghany, 2020; Wagenaar et al., 2019). For example, Genetic Algorithm Rule‐Set Production (GARP) and Quick Unbiased Efficient Statistical Tree (QUEST) are used to map the flood risk considering factors such as population and urban density as well as socioeconomic factors (Darabi et al., 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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The objective of this study is to create and test a hybrid deep learning (DL) model, FastGRNN‐FCN (fast, accurate, stable and tiny gated recurrent neural network‐fully convolutional network), for urban flood prediction and situation awareness using channel network sensors data. The study used Harris County, Texas, as the testbed, and obtained channel sensor data from three historical flood events (e.g., 2016 Tax Day Flood, 2016 Memorial Day Flood, and 2017 Hurricane Harvey Flood) for training and validating the hybrid DL model. The flood data are divided into a multivariate time series and used as the model input. Each input comprises nine variables, including information of the studied channel sensor and its predecessor and successor sensors in the channel network. Precision‐recall curve and F‐measure are used to identify the optimal set of model parameters. The optimal model with a weight of 1 and a critical threshold of 0.59 are obtained through 100 iterations based on examining different weights and thresholds. The test accuracy and F‐measure eventually reach 97.8% and 0.792, respectively. The model is then tested in predicting the 2019 Imelda Flood in Houston and the results show an excellent match with the empirical flood. The results show that the model enables accurate prediction of the spatial–temporal flood propagation and recession and provides emergency response officials with a predictive flood warning tool for prioritizing the flood response and resource allocation strategies.

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Section: Literature Reviewmentioning

confidence: 99%

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Dong

Farahmand

et al. 2020

Computer aided Civil Eng

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“…In the coastal reaches, biological oxygen demands (BOD) have reached 17 mg L −1 , while pH has fallen to 5.3, with higher conductivity (>0.2 ms cm −1 ); total colifom counts as high as 1500 cells 100 mL −1 have been reported even in the middle reaches [37]. Although nutrient spiraling and flushing remediate pollution during the high-discharge wet season (800-1800m 3 s −1 ), pollutant resident time can be much higher during low-flow dry (20-25m 3 s −1 ) season [38,53].…”

Section: Water Pollutionmentioning

confidence: 99%

Challenges in Biodiversity Conservation in a Highly Modified Tropical River Basin in Sri Lanka

Surasinghe

Kariyawasam²,

Sudasinghe

et al. 2019

Water

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Kelani River is the fourth longest river in the South-Asian island, Sri Lanka. It originates from the central hills and flows through a diverse array of landscapes, including some of the most urbanized regions and intensive land uses. Kelani River suffers a multitude of environmental issues: illegal water diversions and extractions, impoundment for hydroelectricity generation, and pollution, mostly from agrochemicals, urban runoff, industrial discharges, and domestic waste. Moreover, loss of riparian forest cover, sand-mining, and unplanned development in floodplains have accentuated the environmental damage. In this study, based on Kelani River basin, we reviewed the status of biodiversity, threats encountered, conservation challenges, and provided guidance for science-based conservation planning. Kelani River basin is high in biodiversity and endemism, which includes 60 freshwater fish species of which 30 are endemic. Urbanization related threats are more severe in the middle and lower reaches while agriculture and impoundments peril the river in upper reaches. Documentation of these threats can be dated back to 1980, yet, Sri Lankan government has failed to take substantial actions for sustainable management of Kelani River basin, despite the presence of nearly 50 legislations pertaining to water and land management. Given high biodiversity richness, human dependency, and evident ecological deterioration, Kelani River basin should be prioritized for biodiversity conservation and sustainable resource management. Conservation and wise use of freshwater resources is a global concern, particularly for developing nations in Asia. Therefore, our review and guidance for scientifically informed conservation would serve as a prototype for basin-wide river management for Sri Lanka as well as for other developing nations of tropical Asia.

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“…Common practice in flood risk mitigation studies relies on individual storm events, either past events or ''design storms,'' ignoring the probability distribution of event occurrence (e.g., Barth and Dö ll 28 and Wagenaar et al 29 ). In contrast to using single events, probabilistic risk analysis attempts to model the entire probability distribution of events, critical for risk-informed decision-making practices such as engineering reliability analysis, insurance, engineering design, construction codes, urban infrastructure planning, or other decisions on the utilization of resources for increased safety.…”

Section: Introductionmentioning

confidence: 99%

“…32 In the case of probabilistic flood risk analysis, the approach propagates natural variability in rainfall, through hydrologic, hydraulic, exposure, and vulnerability models in order to develop loss-recurrence curves measured in terms of direct impacts to communities (i.e., people affected, damage, losses). Wagenaar et al 29 used a 1D2D hydrodynamic model and a probabilistic hazard assessment to calculate the present value of a wetland in Sri Lanka in terms of flood risk reduction. As stated by the authors, theirs is one of the only analyses providing economic calculations for the flood risk reduction benefits of natural ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Nature-based solutions for flood risk reduction: A probabilistic modeling framework

Lallemant¹,

Hamel²,

Balbi³

et al. 2021

One Earth

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Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka

Cited by 31 publications

References 17 publications

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

A hybrid deep learning model for predictive flood warning and situation awareness using channel network sensors data

Challenges in Biodiversity Conservation in a Highly Modified Tropical River Basin in Sri Lanka

Nature-based solutions for flood risk reduction: A probabilistic modeling framework

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