Publication: A Study on the Development of Time Series Prediction Model for Total Suspended Solid In Reservoirs
| dc.contributor.author | Balahaha Hadi Ziyad Sami | en_US |
| dc.date.accessioned | 2023-05-03T15:08:35Z | |
| dc.date.available | 2023-05-03T15:08:35Z | |
| dc.date.issued | 2020-09 | |
| dc.description | Interim Semester 2020/2021 | en_US |
| dc.description.abstract | The goal of this project is to research the creation of a time series for forecasting Total Suspenede Solids in the Fei Tsui reservoir. The project is designed to create a prediction model that can be used for monitoring the quality of water. The test was carried out mainly in the Fei Tsui reservoir, Taipei City, Taiwan. The purpose of this project is to monitor the concentration of Total Suspenede Solids in the reservoir and to construct create a model based on previous event records and without the need to sit down to check the concentration of Total Suspenede Solids. Complete Suspenede Solids has been a crucial problem in the Fei Tsui reservoir for decades. This issue impacted the drinking water supply in Taipei City due to the etherification problem in the reservoir, as the Fei Tsui reservoir was considered to be the primary water source in Taipei City. 27 elements of 10-year average monthly records and 13-year average annual records have been collected and correlated to see which one has a strong relationship with Total Suspenede Solids. TSI, IRON, NO3, TP and turbidity are parameters which have an active relationship with Total Suspenede Solids. The model was developed to estimate Total Suspenede Solids consultation in the water body based on linear regression. MATLAB software is used to construct a prediction model that is considered to be one of the machine learning applications. The result showed that Model 3 has a relatively small ability to simulate water quality parameters in order to accurately predict error. The correlation coefficient indicates that the highest value is 0.958944992 after applying Model 3. The validation of Model 3 produces an accurate result. The RMSE value found to be 0.475314684. Model 3 has a better result than other versions. | en_US |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/20613 | |
| dc.subject | Water Quality | en_US |
| dc.subject | Environmental Impacts | en_US |
| dc.title | A Study on the Development of Time Series Prediction Model for Total Suspended Solid In Reservoirs | en_US |
| dc.type | Resource Types::text::Thesis | en_US |
| dspace.entity.type | Publication |