IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR Asia Pacific Division Proceedings : 19th IAHR APD Congress, Hanoi 2014 : Session 1: ENVIRONMENTAL HYDRAULICS : Short-term Prediction of Chlorophyll-a in a Eutrophic Closed Water Body using Chaos Recurrent Neural...
Short-term Prediction of Chlorophyll-a in a Eutrophic Closed Water Body using Chaos Recurrent Neural Network
Field measurement methods for chlorophyll-a concentrations have been developed, enabling data collection in a short time scale because of recent technological advancements in optical sensors. Such time-series data provide important find-ings in analyses and predictions of aquatic environments targeting eutrophic water areas. In this study, the water quality dynamics in a eutrophic reservoir in a flat low-lying agricultural area was analyzed from the viewpoint of short-time prediction of time series data using artificial intelligence to assess the water environmental dynamics related to a phyto-plankton. Specifically, we proposed a short-term prediction method for chlorophyll-a concentrations with a chaos recur-rent neural network based on continuous observation data. This study aimed to improve the degree of prediction accura-cy with observation noise processing, which uses wavelet analysis and reinforcement learning with supplemental training data, and then examined the effectiveness of the prediction method. As a result, by introducing the noise processing, the accuracy of the predictions improved significantly and the practicality of the method proposed in this study increased, as evidenced by the fact that the lead time exceeded 48 h. In particular, it is suggested that predications are feasible with the same degree of accuracy for each lead time within the limit lead time. Yet distinctive variation patterns emerge with regard to changes in the algae species composition caused by artificial effects during the period in which irregular water level management takes place. Therefore, it was difficult to conduct real-time predictions for chronological changes that share no similarities with such learning data.
File Size : 2,398,207 bytes
File Type : Microsoft Word Document
Chapter : IAHR Asia Pacific Division Proceedings
Category : 19th IAHR APD Congress, Hanoi 2014
Date Published : 10/03/2015
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