Developing sustainable strategies by LID optimization in response to annual climate change impacts

Zamani, Mohammad Ghadir; Saniei, Khashayar; Nematollahi, Banafsheh; Zahmatkesh, Zahra; Poor, Mohammad Moghadari; Nikoo, Mohammad Reza

doi:10.1016/j.jclepro.2023.137931

Cited by 19 publications

(4 citation statements)

References 100 publications

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“…Optimization of LID technologies under various climate scenarios is also studied by a few researchers. One such study, [12] discusses improved rainwater quality using LIDs under various climate scenarios. The optimization procedure of bio-retention systems is discussed by [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Low Impact Development-An effective tool towards urban flood resilience

Ambily,

Reddy,

Ganesh

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The uncertainties created by climate change and the pressure of urbanisation create unparalleled challenges to urban disaster management. Conventional solutions to urban infrastructure management are no longer sufficient to combat emerging challenges. Among all the disasters, floods require special attention because of the destruction it is causing to the economy and their frequency of occurrence. Managing urban pluvial floods is causing a real challenge due to the uncertainties about the source and direction of flow and the insufficiency of grey infrastructure. Low Impact Development (LID) technologies are recognised by many researchers as an effective method to support the existing grey infrastructure. In this paper, the authors analyse and understand the effectiveness of LID techniques in creating urban flood resilience. A frequently flooded region in the south-Indian, coastal city of Kochi is selected as the study region. The results indicate that, among the three experimented options of porous pavements, bio-retention cells and rain barrels, permeable pavements created the highest reduction in the run-off if used in isolation. However, a combination of various technologies is found to be more efficient than localised attempts in a micro-scale. The study also found that the efficacy of LID technologies depends on the local characteristics, including topography, soil type and spatial availability. The research can be further carried out using more LID technologies and linking it with the existing drainage network.

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Section: Literature Reviewmentioning

confidence: 99%

Low Impact Development-An effective tool towards urban flood resilience

Ambily,

Reddy,

Ganesh

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Their results showed that proper implementation of permeable pavements reduced the flow of surface runoff by between 44 and 92%. Numerous studies focusing on green infrastructure implementation to mitigate the impacts of flooding have been conducted [21][22][23][24][25][26]. However, it is essential to recognize that although Low Impact Development (LID) systems can effectively manage stormwater, their efficacy could be constrained during heavy rainfall events.…”

Section: Introductionmentioning

confidence: 99%

Iterative Search Space Reduction (iSSR) for Optimal Flood Control in Urban Drainage Networks

Ngamalieu-Nengoue,

Iglesias-Rey,

Martínez-Solano

et al. 2024

Water

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Extreme rainfall events cause immense damage in cities where drainage networks are nonexistent or deficient and thus unable to transport rainwater. Infrastructure adaptations can reduce flooding and help the population avoid the associated negative consequences. Consequently, it is imperative to develop suitable mathematical models rooted in a thorough understanding of the system. Additionally, the utilization of efficient computational search techniques is crucial when applying these methods to real-world problems. In this study, we propose a novel iterative search space reduction methodology coupled with a multiobjective algorithm (NSGA-II) for urban drainage network rehabilitation and flood mitigation. This approach considers the replacement of pipes and the installation of storm tanks (STs) in drainage networks. Additionally, NSGA-II is integrated with the Storm Water Management Model (SWMM) to achieve multiobjective optimization. To demonstrate the advantages of using this technique, two case study networks are presented. After three iterations, 90% of the decision variables are eliminated from the process in the E-Chicó case, and 76% are eliminated in the Ayurá case. The primary outcome of this study is that the proposed methodology yields reductions in rehabilitation costs and flood levels. Additionally, the application of NSGA-II to the reduced-dimension model of the network yields a superior Pareto front compared to that of the original network.

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“…Urban areas threaten the purity of lakes and reservoirs because of the unauthorized release of industrial water and household sewage, which often contaminates these bodies of water 1 , 2 . The deterioration of water quality can have several detrimental effects, including increased human exposure to disease and harmful chemicals 3 , 4 , a decrease in ecosystem productivity and biodiversity 5 , and harm to water-related industries, such as aquaculture and agriculture 6 .…”

Section: Introductionmentioning

confidence: 99%

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

Nikoo,

Zamani,

Zadeh

et al. 2024

Sci Rep

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In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

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Developing sustainable strategies by LID optimization in response to annual climate change impacts

Cited by 19 publications

References 100 publications

Low Impact Development-An effective tool towards urban flood resilience

Low Impact Development-An effective tool towards urban flood resilience

Iterative Search Space Reduction (iSSR) for Optimal Flood Control in Urban Drainage Networks

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

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