Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories

Ghodsi, Seyed Hamed; Kerachian, Reza; Malakpour‐Estalaki, Siamak; Nikoo, Mohammad Reza; Zahmatkesh, Zahra

doi:10.1016/j.jhydrol.2015.11.045

Cited by 53 publications

(10 citation statements)

References 35 publications

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“…Different permutations of

are calculated and the minimum and maximum projected k values are taken, respectively, as the center and upper bound for the Info-gap method [ 31 ]. Regarding different ranges of robustness value for optimal scenarios, these values are normalized relating to maximum probable robustness value [ 32 ]:

Where

represents the normalized robustness.…”

Section: Methodsmentioning

confidence: 99%

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Ranjbar

Cherubini

2020

Heliyon

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The aim of this study is to develop an accurate and reliable numerical model of the coastal Talar aquifer threatened by seawater intrusion by developing an ensemble meta-model (MM). In comparison with previous methodologies, the developed model has the following superiority: (1) Its performance is enhanced by developing ensemble MMs using four different meta-modelling frameworks, i.e., artificial neural network, support vector regression, radial basis function, genetic programing and evolutionary polynomial regression; (2) The accuracy of different MMs based on 16 integration of four meta-modeling frameworks is compared; and (3) the effect of aquifer heterogeneity on the MM. The performance of the proposed MM was assessed using an illustrative case aquifer subject to seawater intrusion. The obtained results indicate that the ensemble MM that combines all four meta-modeling frameworks outperformed the GP and ANN models, with a correlation coefficient of 0.98. Moreover, the proposed MM using nonlinear-learning ensemble of SVR-EPR achieves a better and robust forecasting performance. Therefore, it can be considered as an accurate and robust simulator to predict salinity levels under different abstraction patterns in variable density flow. The result of uncertainty analyses reveals that robustness value and pumping rate are inversely proportional and scenarios with a robustness measure of about 12% are more reliable.

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“…Different permutations of

Where

represents the normalized robustness.…”

Section: Methodsmentioning

confidence: 99%

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Ranjbar

Cherubini

2020

Heliyon

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“…Many of the case studies (31 of 64) used the simpler 1D or 1D/2D modeling to understand performance of green infrastructure and impact of flooding (e.g., Casal‐Campos et al., 2015; Ghodsi et al., 2016; Kim et al., 2017; Kirshen et al., 2015; Mei et al., 2018; Moore et al., 2016; Ramm et al., 2018a; M. Wang et al., 2017, 2019). The most popular among these models was the Storm Water Management Model (SWMM), an open source model developed and maintained by U.S. EPA scientists (Niazi et al., 2017).…”

Section: Green Infrastructure Adaptation Strategies Within Dmdu Appro...mentioning

confidence: 99%

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

Webber

Samaras

2022

Earth's Future

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Decision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

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“…As demonstrated in previous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], uncertain optimization techniques are suitable in tackling water quality management problems, which included stochastic mathematical programming (SMP), fuzzy mathematical programming (FMP), and interval linear programming (ILP), as well as their integrations. Among above optimization approaches, inexact two-stage stochastic programming (ITSP) model proposed by Huang and Loucks [18] was frequently applied in water management fields [15,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 98%

A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

Huang

2016

Mathematical Problems in Engineering

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Nonpoint source (NPS) pollution caused by agricultural activities is main reason that water quality in watershed becomes worse, even leading to deterioration. Moreover, pollution control is accompanied with revenue’s fall for agricultural system. How to design and generate a cost-effective and environmentally friendly agricultural production pattern is a critical issue for local managers. In this study, a risk-based interval two-stage programming model (RBITSP) was developed. Compared to general ITSP model, significant contribution made by RBITSP model was that it emphasized importance of financial risk under various probabilistic levels, rather than only being concentrated on expected economic benefit, where risk is expressed as the probability of not meeting target profit under each individual scenario realization. This way effectively avoided solutions’ inaccuracy caused by traditional expected objective function and generated a variety of solutions through adjusting weight coefficients, which reflected trade-off between system economy and reliability. A case study of agricultural production management with the Tai Lake watershed was used to demonstrate superiority of proposed model. Obtained results could be a base for designing land-structure adjustment patterns and farmland retirement schemes and realizing balance of system benefit, system-failure risk, and water-body protection.

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Developing a stochastic conflict resolution model for urban runoff quality management: Application of info-gap and bargaining theories

Cited by 53 publications

References 35 publications

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

Development of a robust ensemble meta-model for prediction of salinity time series under uncertainty (case study: Talar aquifer)

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

A Risk-Based Interval Two-Stage Programming Model for Agricultural System Management under Uncertainty

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