Risk assessment of river water quality using long-memory processes subject to divergence or Wasserstein uncertainty

River water quality often follows a long-memory stochastic process with power-type autocorrelation decay, which can only be reproduced using appropriate mathematical models. The selection of a stochastic process model, particularly its memory structure, is often subject to misspecifications owing to...

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Vydáno v:	Stochastic environmental research and risk assessment Ročník 38; číslo 8; s. 3007 - 3030
Hlavní autoři:	Yoshioka, Hidekazu, Yoshioka, Yumi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024 Springer Nature B.V
Témata:	Aquatic Pollution Chemical oxygen demand Chemistry and Earth Sciences Computational Intelligence Computer Science Deviation Divergence Earth and Environmental Science Earth Sciences Environment Environmental assessment Environmental risk Math. Appl. in Environmental Science Mathematical models Nitrogen dioxide Organic carbon Organic phosphorus Original Paper Physics Probability Theory and Stochastic Processes Risk assessment Rivers Seasonal variations Statistics for Engineering Stochastic models Stochastic processes Total organic carbon Uncertainty Waste Water Technology Water Management Water Pollution Control Water quality Wasserstein ball Long-memory process Water quality dynamics Divergence risk measure Upper deviation
ISSN:	1436-3240, 1436-3259
On-line přístup:	Získat plný text
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Popis
Shrnutí:	River water quality often follows a long-memory stochastic process with power-type autocorrelation decay, which can only be reproduced using appropriate mathematical models. The selection of a stochastic process model, particularly its memory structure, is often subject to misspecifications owing to low data quality and quantity. Therefore, environmental risk assessment should account for model misspecification through mathematically rigorous and efficiently implementable approaches; however, such approaches have been still rare. We address this issue by first modeling water quality dynamics through the superposition of an affine diffusion process that is stationary and has a long memory. Second, the worst-case upper deviation of the water quality value from a prescribed threshold value under model misspecifications is evaluated using either the divergence risk or Wasserstein risk measure. The divergence risk measure can consistently deal with the misspecification of the memory structure to the worst-case upper deviation. The Wasserstein risk measure is more flexible but fails in this regard, as it does not directly consider the memory structure information. We theoretically compare both approaches to demonstrate that their assumed uncertainties differed substantially. From the application to the 30-year water quality data of a river in Japan, we categorized the water quality indices to be those with truly long memory (Total nitrogen, NO 3 -N, NH 4 -N, and SO 4 2 - ), those with moderate power-type memory (NO 2 -N, PO 4 -P, and Total Organic Carbon), and those with almost exponential memory (Total phosphorus and Chemical Oxygen demand). The risk measures are successfully computed numerically considering the seasonal variations of the water quality indices.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1436-3240 1436-3259
DOI:	10.1007/s00477-024-02726-y