Author: |
呼延建 Zamroji Hariyanto |
---|---|
Thesis Title: |
運用深度神經網絡技術建構最佳化預測模式 - 以PM2.5感測資料集為例 Construction of Optimized Forecasting Model Using Deep Neural Networks Technology Based on PM2.5 Sensed Dataset |
Advisor: |
羅乃維
Nai-Wei Lo |
Committee: |
賴源正
Yuan-Cheng Lai 林伯慎 Bor-Shen Lin |
Degree: |
碩士 Master |
Department: |
管理學院 - 資訊管理系 Department of Information Management |
Thesis Publication Year: | 2019 |
Graduation Academic Year: | 107 |
Language: | 英文 |
Pages: | 73 |
Keywords (in Chinese): | PM2.5 、Forecasting Optimization 、Data Cleaning 、Deep Neural Network 、Deep Learning |
Keywords (in other languages): | PM2.5, Forecasting Optimization, Data Cleaning, Deep Neural Network, Deep Learning |
Reference times: | Clicks: 532 Downloads: 4 |
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Technology in human life has advanced tremendously and it brings a lot of convenient for people in various aspects of their life. Besides that, it also brings a harmful impact on the environment, especially on air quality. Due to industrial production, the quantity of pollutant concentration raises rapidly many times. Fine particulate matter (PM2.5), one of dangerous pollutant, is regarded as one of the main factors for the deterioration of public health. Many efforts were being created to provide the monitoring of PM2.5 concentrations. PM2.5 forecasting provided for early warning to people. In terms of forecasting, accuracy is the most challenging task. A proper model needs to be constructed to lead the precision prediction. Nowadays, Deep Neural Network (DNN) is an artificial intelligence technique that has proven to solve several prediction problems. Therefore, this thesis proposed the forecasting optimization mechanism employing the Golden Section Search and Fruit Fly Optimization Algorithm combines with a data cleansing mechanism using DNN models. The proposed mechanism effectively optimize three DNN models that are Multilayer Perceptron (MLP), Long Short – Term Memory (LSTM) and Gated Recurrent Unit (GRU) to achieve better forecasting accuracy of PM2.5 concentration.
Technology in human life has advanced tremendously and it brings a lot of convenient for people in various aspects of their life. Besides that, it also brings a harmful impact on the environment, especially on air quality. Due to industrial production, the quantity of pollutant concentration raises rapidly many times. Fine particulate matter (PM2.5), one of dangerous pollutant, is regarded as one of the main factors for the deterioration of public health. Many efforts were being created to provide the monitoring of PM2.5 concentrations. PM2.5 forecasting provided for early warning to people. In terms of forecasting, accuracy is the most challenging task. A proper model needs to be constructed to lead the precision prediction. Nowadays, Deep Neural Network (DNN) is an artificial intelligence technique that has proven to solve several prediction problems. Therefore, this thesis proposed the forecasting optimization mechanism employing the Golden Section Search and Fruit Fly Optimization Algorithm combines with a data cleansing mechanism using DNN models. The proposed mechanism effectively optimize three DNN models that are Multilayer Perceptron (MLP), Long Short – Term Memory (LSTM) and Gated Recurrent Unit (GRU) to achieve better forecasting accuracy of PM2.5 concentration.
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