| Author: | Wang, Quan |
| Title: | Neural network and genetic programming for modeling coastal algal blooms in Tolo Harbour, Hong Kong |
| Advisors: | Chau, Kwok-wing (CEE) |
| Degree: | M.Sc. |
| Year: | 2016 |
| Subject: | Hong Kong Polytechnic University -- Dissertations Algal blooms -- Monitoring -- China -- Hong Kong Water quality -- China -- Hong Kong |
| Department: | Faculty of Construction and Environment |
| Pages: | x, 88 pages : color illustrations |
| Language: | English |
| Abstract: | Water is a special resource on the earth, which cannot be replaced by any other material. Water is a unique physical and chemical properties. In a word, nobody can live without water. However, the living condition are facing with the problem of water resource shortage and deterioration of water quality. Especially, harmful algal bloom (HAB) is regarded as a serious environmental problem in Tolo Harbour, Hong Kong. In recent years, forecasting of water quality has become one of the hottest point of most scholar. Based on the non-determinacy and highly non-linearity characteristic of the water environment pollution processing, machine learning (ML) techniques have been more and more used to model the progress of algal dynamics. One of the ML method, artificial neural network (ANN), is used to predict the algal blooms of the coastal waters of Tolo Harbour. And the model we set up is based on a biweekly water quality data of two monitors TM3 and TM4 at Tolo Harbour (2004-2014). Analysis of different combination of input scenarios shows that the smaller variables input, the better agreement with observations is obtained. And the finding is in contrast to previous study which suggested that more complicated neural networks is goodness for algal blooms predation in freshwater systems. Then, the study of the various scenarios weights of trained neural network and genetic programming shows that well prediction of long time period of algal dynamics will be got when the input is only chlorophyll-a. What is more, the consequence of the study also suggest that the prediction of algal biomass based on the biweekly record data have good performance on long-term trends, but the prediction on short-term trends is still need more frequency record data. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991021952829903411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.32 MB | Adobe PDF | View/Open |
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