Three-layered Feedforward artificial neural network with dropout for short-term prediction of class-differentiated Chl-a based on weekly water-quality observations in a eutrophic agricultural reservoir

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
JournalPaddy and Water Environment
DOIs
Publication statusAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Agronomy and Crop Science
  • Water Science and Technology

Fingerprint

Dive into the research topics of 'Three-layered Feedforward artificial neural network with dropout for short-term prediction of class-differentiated Chl-a based on weekly water-quality observations in a eutrophic agricultural reservoir'. Together they form a unique fingerprint.

Cite this