Uncertainty analysis using fuzzy models in hydroinformatics

Boulmaiz, Tayeb; Guermoui, Mawloud; Saber, Mohamed; Boutaghane, Hamouda; Abida, Habib; Eslamian, Saeid

doi:10.1016/b978-0-12-821285-1.00002-6

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…The US Environmental Protection Agency (EPA) has developed a national stormwater calculator to estimate the annual amount of rainwater and frequency of runoff from a specific site (EPA National Stormwater Calculator, n.d.). ScienceDirect has compiled a list of papers under the title "Urban Flooding" (Atta-ur-Rahman et al, 2016;Boulmaiz et al, 2023;Cook et al, 2019;Eldho et al, 2018;Lopez-Gunn et al, 2021;Meng, 2018;Myers & Pezzaniti, 2019;Sebastian et al, 2022;Sinha, 2023;Upreti et al, 2024). These papers discuss a range of issues related to urban flooding, including urban flood management and relationships to climate change and urban flooding.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The first author undertook a number of flooding analysis using these software tools and reported the results in his dissertation (Ekeh, 2020). Most of the available literature (Atta-ur-Rahman et al, 2016;Boulmaiz et al, 2023;Cook et al, 2019;Eldho et al, 2018;Lopez-Gunn et al, 2021;Meng, 2018;Myers & Pezzaniti, 2019;Sebastian et al, 2022;Sinha, 2023;Upreti et al, 2024) and the hydrologic software tools offer solutions to urban flooding either in a qualitative way or by iterating through a series of manual steps. This makes the process of identifying attributes causing flooding very inefficient.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing Urban Flooding by Optimal Design of Drainage System Using Sequential Least Squares Quadratic Programming and Spatial Datasets

Ekeh,

Jha

2024

JCCE

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Urban flooding is caused due to poor drainage design and excessive rain. It severely affects the road infrastructure. Existing hydrologic software tools to examine the extent of urban flooding primarily require walking through a series of manual steps and address each study area individually, preventing a collective review of poor storm-drains in an efficient manner. Previous methods for optimal drainage design were inefficient and lacked the ability of solving the underlying optimization problem due to the inherent nonlinearity of the decision variables. In this paper, we develop a nonlinear optimization formulation to minimize urban flooding using underground pipe size as a decision variable. We propose a solution algorithm using sequential least squares quadratic programming and spatial datasets. The proposed method eliminates the need to examine each study area manually using existing hydrologic tools. An example using the storm-drain system for the Baltimore County is performed. The results show that the model is effective in identifying storm-drain deficiencies and correcting them by choosing appropriate storm-drain inlet types to minimize flooding. Future works may include using large datasets and a more sophisticated modeling approach for estimating rainfall intensity based on extreme weather patterns. The method can be applied to other jurisdictions if relevant hydrological and underdrain piping network data were available.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimizing Urban Flooding by Optimal Design of Drainage System Using Sequential Least Squares Quadratic Programming and Spatial Datasets

Ekeh,

Jha

2024

JCCE

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“…Accurate dataset classification is a crucial task in data mining and machine learning, enabling effective decision-making across various domains (Jayatilake & Ganegoda, 2021) (Bock et al, 2019) (Swathy & Saruladha, 2022) (Saravi et al, 2022). Traditional classification methods are often inadequate in handling fuzzy data, which is characterized by uncertainty, imprecision, and vagueness (Boulmaiz et al, 2023). Existing approaches struggle to capture the complexities of fuzzy data, leading to suboptimal classification results.…”

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confidence: 99%

Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification

Joseph¹

2023

emod

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Accurate dataset classification is a critical task in various domains, and combining different methodologies can enhance classification performance. This research presents a novel approach that integrates Hybrid Grid Partition and Rough Set methods for fuzzy rule generation, aiming to improve accuracy and interpretability in dataset classification. The proposed approach leverages Hybrid Grid Partition to discretize continuous attributes and Rough Set attribute reduction to identify essential attributes, enabling accurate classification while handling uncertainty and imprecision. The generated fuzzy rules provide interpretability, aiding decision-making processes and providing insights into classification factors. The approach's robustness and generalization capabilities are demonstrated through experiments on diverse datasets, indicating its potential applicability in real-world scenarios. However, limitations such as the absence of specific evaluation metrics and the need for further validation on larger datasets are acknowledged. Overall, this research contributes to accurate dataset classification by offering a novel integrated approach and highlighting areas for future investigation and refinement

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Uncertainty analysis using fuzzy models in hydroinformatics

Cited by 2 publications

References 48 publications

Minimizing Urban Flooding by Optimal Design of Drainage System Using Sequential Least Squares Quadratic Programming and Spatial Datasets

Minimizing Urban Flooding by Optimal Design of Drainage System Using Sequential Least Squares Quadratic Programming and Spatial Datasets

Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification

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