Optimal design of low impact development practices in response to climate change

Ghodsi, Seyed Hamed; Zahmatkesh, Zahra; Goharian, Erfan; Kerachian, Reza; Zhu, Zhenduo

doi:10.1016/j.jhydrol.2019.124266

Cited by 86 publications

(23 citation statements)

References 35 publications

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“…Examples of such interventions are expansions of the sewer system capacity or replacements of piped sections with self‐cleansing sewer pipes that guarantee sediment transport (Montes, Berardi, Kapelan, & Saldarriaga, 2020; Montes, Kapelan, & Saldarriaga, 2019). Alternatively, Sustainable Urban Drainage Systems (Ghodsi, Zahmatkesh, Goharian, Kerachian, & Zhu, 2020; Torres, Fontecha, Zhu, Walteros, & Rodríguez, 2020) can be placed to alleviate the sediments load from runoff (Maringanti, Chaubey, & Popp, 2009). From a larger perspective, results presented herein constitute a building block to the planning of urban renewal interventions.…”

Section: Discussionmentioning

confidence: 99%

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly rightskewed sewer system failure data to predict the failure risk, leveraging a bidimensional spacetime approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodnessof-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

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Section: Discussionmentioning

confidence: 99%

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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“…Climate projections from five general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive were obtained from the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP; Forster et al., 2013; Ghodsi et al., 2020); this project provides spatially downscaled climate data for impact models and implements a bias correction for the projected climate variables using observations (Zhang et al., 2015; Zhou et al., 2018). A representative concentration pathway (RCP 8.5), the business‐as‐usual scenario, was selected as an example to explore the total flooding volume and the changing characteristics of its spatial distribution.…”

Section: Methodsmentioning

confidence: 99%

“…Analysis of urban flooding has been taken extensively over the past decade (Bates et al., 2010; Miller et al., 2014; Z. Yin et al., 2011). Previous studies usually adopted urban flood models (e.g., Storm Water Management Model: SWMM, MIKE URBAN, and InfoWorks CS) to explore different aspects of urban flooding and their influencing factors (Ramachandraiah, 2011), spatial and temporal patterns (Huang et al., 2018; Kalantari et al., 2014), associated damages (Jamali et al., 2018; Liu & Nan, 2002) and risk of urban flooding (Amaguchi et al., 2012; Ghodsi et al., 2020; Zhang & Pan, 2014), based on historical observed data. For example, Tang et al.…”

Section: Introductionmentioning

confidence: 99%

Simulation and Assessment of Projected Climate Change Impacts on Urban Flood Events: Insights From Flooding Characteristic Metrics

et al. 2022

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Urban flooding is a severe disaster with considerable casualties, economic losses, and social impacts worldwide (Georgi et al., 2016;Lin et al., 2018;Qiang, 2019). According to the European Environment Agency report on urban adaptations to climate change in 2016, flooding has caused massive losses of economic assets in many European cities. For example, the flood damage to the community services in Dresden, Germany in 2002 was approximately 80 million euros (Georgi et al., 2016). In the USA, New York City has been hit by several extreme rainfall events, and the massive flood that occurred in 2012 claimed 45 lives and caused more than $20 billion in flood-associated damage to the city's buildings and infrastructure (Yin, Lin, & Yu, 2016;. Urban flooding has also frequently occurred in many cities in China, resulting in traffic paralysis and the inundation of residential and public buildings. In 2016, severe flooding occurred in many of the southern cities in China, killing 300 people, forcing over half a million to evacuate, and causing over $44.7 billion in economic losses (Shepard, 2016). In the next few decades, flood damages in urban areas are likely to be aggravated due to global trends in urbanization and climate change (Kundzewicz et al., 2014).Analysis of urban flooding has been taken extensively over the past decade (Bates et al., 2010;Miller et al., 2014;Z. Yin et al., 2011). Previous studies usually adopted urban flood models (e.g., Storm Water Management Model: SWMM, MIKE URBAN, and InfoWorks CS) to explore different aspects of urban flooding and their influencing factors (

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“…e intelligent optimization algorithms with powerful search ability are used to find the best LIDs layout scheme. Ghodsi et al [17] propose a multievent single objective genetic algorithm, which considers the optimal solution of runoff volume and LIDs cost under various climate scenarios. According to their previous research, the weight of LIDs construction cost, runoff quantity and runoff quality could be set as 0.3, 0.35 and 0.35, respectively, when they are regarded as equally important objectives for optimization [18].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City

Men

Jiang

et al. 2020

Mathematical Problems in Engineering

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Aiming at the optimization layout of distributed low-impact development (LID) practices in the sponge city, a new mathematical method combining Stormwater Management Model (SWMM) and preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) was developed and was applied in the Ximen waterlogged area of Pingxiang City. Firstly, a block-scaled rainfall-runoff model was built in the study area by using SWMM. Then, an LIDs area optimization model was established by linking the SWMM and the PICEA-g based on the Matlab platform, which took the area ratios of various LIDs in each block as decision variables and took the total runoff, peak flow, suspended substance (SS) pollutant, and LIDs cost as objective functions. Thus, the problem of LIDs layout was turned into a mathematical optimization issue. So the cost-benefit optimal solutions with different emphases were found by using this algorithm, and the LIDs layout optimal scheme for this area was further analysed and verified by rainfall-runoff model. The results show that the total runoff reduction rates of the system reach a maximum of 21.8%, the peak flow reduction rates of the system are more than 10%, and the SS pollutant reduction rates are reduced by about 30% compared with before LIDs under the design storms of different return periods. The reduction rates of each runoff index are higher than the nondominated sorting genetic algorithm II (NSGA-II) method, and decision-makers can more effectively analyse the cost-benefit optimal solution from the Pareto solution sets. Therefore, the LIDs layout optimization method proposed in this paper has obvious advantages in solving similar many-objective optimization problems (MOOPs) in sponge city construction.

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Optimal design of low impact development practices in response to climate change

Cited by 86 publications

References 35 publications

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

Simulation and Assessment of Projected Climate Change Impacts on Urban Flood Events: Insights From Flooding Characteristic Metrics

Mathematical Optimization Method of Low-Impact Development Layout in the Sponge City

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