Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F22%3A00357899" target="_blank" >RIV/68407700:21110/22:00357899 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3390/w14060920" target="_blank" >https://doi.org/10.3390/w14060920</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/w14060920" target="_blank" >10.3390/w14060920</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Popis výsledku v původním jazyce

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea

Název v anglickém jazyce

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Popis výsledku anglicky

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10503 - Water resources

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Water

ISSN

2073-4441

e-ISSN

2073-4441

Svazek periodika

14

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

21

Strana od-do

—

Kód UT WoS článku

000774430900001

EID výsledku v databázi Scopus

2-s2.0-85127334644