E-resources
Peer reviewed
-
Yamamoto, Ren; Harada, Masayoshi; Hiramatsu, Kazuaki; Tabata, Toshinori
Paddy and water environment, 2022/1, Volume: 20, Issue: 1Journal Article
To effectively manage a eutrophic reservoir, short-term predictions of algae class-differentiated chlorophyll a (Chl-a) were conducted. The study adopted a three-layered feedforward artificial neural network using discrete water environment datasets collected through weekly observations from May to November over seven years (2012–2018). This network was constructed using supervised learning, and the available datasets of a certain observation day were set as the input variables to determine the total Chl-a, Chlorophyceae Chl-a, and cyanobacteria Chl-a that would exist after one week. From the viewpoint of the simplification of the network’s complexity to suppress overfitting, input variables were carefully selected by identifying the important variables related to the seasonal changes in Chlorophyceae and cyanobacteria and eliminating the duplicated expressions of water-quality parameters. However, network downsizing was found insufficient to suppress overfitting. To improve prediction accuracy, dropout was introduced, which stochastically deactivated some nodes in the input and hidden layers in the learning process. The analysis results showed that sufficient short-term predictions of total Chl-a and Chlorophyceae Chl-a may be achieved. The insufficient prediction accuracy of cyanobacteria Chl-a may be overcome using meteorological data as close as possible to the desired prediction day. Consequently, this model may serve as a useful tool for the management of eutrophic reservoirs because short-term predictions of the dominant phytoplankton can be achieved, and the necessary mitigation measures may be accordingly planned.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Shelf entry
Permalink
- URL:
Impact factor
Access to the JCR database is permitted only to users from Slovenia. Your current IP address is not on the list of IP addresses with access permission, and authentication with the relevant AAI accout is required.
Year | Impact factor | Edition | Category | Classification | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Select the library membership card:
If the library membership card is not in the list,
add a new one.
DRS, in which the journal is indexed
Database name | Field | Year |
---|
Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
---|
Source: Personal bibliographies
and: SICRIS
The material is available in full text. If you wish to order the material anyway, click the Continue button.