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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10503 - Water resources

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

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

Name of the periodical

Water

ISSN

2073-4441

e-ISSN

2073-4441

Volume of the periodical

14

Issue of the periodical within the volume

6

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

—

UT code for WoS article

000774430900001

EID of the result in the Scopus database

2-s2.0-85127334644