Evaluation of Multi- and Many-Objective Optimization Techniques to Improve the Performance of a Hydrologic Model Using Evapotranspiration Remote-Sensing Data

Herman, Matthew R.; Hernandez‐Suarez, J. Sebastian; Nejadhashemi, A. Pouyan; Kropp, Ian; Sadeghi, Ali

doi:10.1061/(asce)he.1943-5584.0001896

Cited by 15 publications

(2 citation statements)

References 46 publications

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“…This algorithm is an extension of the NSGA-III algorithm [80], which is a population-based method based on reference directions, non-domination sorting, and evolutionary operators (i.e., recombination and mutation) for identifying Pareto-optimal solutions. It is worth mentioning that these algorithms have been successfully applied in water resources and crop modeling problems [81][82][83]. The U-NSGA-III algorithm was implemented in python 3.7 using the pymoo library [84].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Multidimensional Aspects of Sustainable Biofuel Feedstock Production

et al. 2021

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Bioenergy is becoming increasingly relevant as an alternative to fossil fuels. Various bioenergy feedstocks are suggested as environmentally friendly solutions due to their positive impact on stream health and ability to sequester carbon, but most evaluations for bioenergy feedstocks have not evaluated the implications of bioenergy crop production holistically to date. Through the application of multi-objective optimization on 10 bioenergy feedstock rotations in a Michigan watershed, a Pareto front is searched to identify optimal trade-off solutions for three objective functions representing stream health, environmental emissions/carbon footprint, and economic feasibility. Various multi-criteria decision-making techniques are then applied to the resulting Pareto front to select a set of most-preferred trade-off solutions, which are compared to optimal solutions from each individual objective function. The most-preferred trade-off solutions indicate that a diverse mix of rotations are necessary to optimize all three objectives, whereas the individually optimal solutions do not consider a diverse range of feedstocks, thereby making the proposed multi-objective treatment an important and pragmatic strategy.

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Section: Multi-objective Optimizationmentioning

confidence: 99%

Multidimensional Aspects of Sustainable Biofuel Feedstock Production

et al. 2021

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“…However, many practitioners in the field of water resources prefer a unique solution, that identifies a good compromise among contrasting counterparts (Tian et al, 2019). The second approach combines multiple objectives into a single metric, which is calculated as weighted sum of multiple metrics based on multiple responses of a hydrograph (Ahmadi et al, 2014;Herman et al, 2020;Huang, 2014;Tian et al, 2019). For instance, some of the researchers suggested to use the Euclidian distance of different metrics from the ideal solution to obtain the optimized performance in all metrics (Fovet et al, 2015;Gupta et al, 2009;Nijzink et al, 2018;Pfannerstill et al, 2014Pfannerstill et al, , 2017Schoups et al, 2005).…”

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confidence: 99%

Formulation of Wavelet Based Multi‐Scale Multi‐Objective Performance Evaluation (WMMPE) Metric for Improved Calibration of Hydrological Models

Manikanta

Vema

2022

Water Resources Research

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Hydrological models are essential tools for assessing, analyzing and developing solutions for natural hydrologic systems (Mostafaie et al., 2018;Wagener et al., 2001). One of the steps in application of these models, is the estimation of their parameters, which is known as model calibration (Guinot et al., 2011;. Parameters of the hydrological models are estimated by comparing the simulated and observed variables of interest (usually streamflow) . The match between simulated and observed variables is expressed using statistical performance measures such as Nash Sutcliffe Efficiency (NSE)

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