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研究生： 呼延建
Zamroji Hariyanto
論文名稱： 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
指導教授： 羅乃維
Nai-Wei Lo
口試委員: 賴源正
Yuan-Cheng Lai
林伯慎
Bor-Shen Lin
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 73
中文關鍵詞： PM2.5 、Forecasting Optimization 、Data Cleaning 、Deep Neural Network 、Deep Learning
外文關鍵詞： PM2.5, Forecasting Optimization, Data Cleaning, Deep Neural Network, Deep Learning
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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.

    Cover i Recommendation Letter ii Qualification Letter iii Abstract iv Acknowledgments v Contents vi List of Figures viii List of Tables x Chapter 1 Introduction 1 Chapter 2 Literature Review 5 2.1 Related Work 5 2.2 Multilayer Perceptron 6 2.3 Recurrent Neural Network 7 2.3.1 Long Short-Term Memory 8 2.3.2 Gated Recurrent Unit 9 2.4 Golden Section Search 10 2.5 Fruit Fly Optimization Algorithm 11 2.6 Anomaly Detection 14 Chapter 3 Proposed Forecasting Model Optimization Mechanism 15 3.1 Basic Forecasting Model Optimization Mechanism 15 3.1.1 Data Observation 15 3.1.2 Dataset Pre-processing 17 3.1.3 Forecasting Model Architecture 18 3.1.4 Model Optimization Process 20 3.2 Forecasting Model Optimization Mechanism with Data Cleaning 21 Chapter 4 Experiment and Results 25 4.1 Environment and Setup 25 4.1.1 Dataset Parameters 25 4.1.2 Model Learning Parameters 26 4.1.3 Optimization Parameters 27 4.2 Experimental Scenarios and Results 28 4.2.1 Scenario 1: Forecasting Model with standard hyper-parameters 29 4.2.2 Scenario 2: Forecasting Model with optimized hyper-parameters 30 4.2.3 Scenario 3: Forecasting Model with optimized hyper-parameters and removing each anomaly data 36 4.2.4 Scenario 4: Forecasting Model with optimized hyper-parameters and removing each anomaly data and the data which in its radius 39 4.2.5 Scenario 5: Forecasting Model with optimized hyper-parameters and replacing anomaly data with its radius mean 41 4.3 Comparison of Five Scenarios 43 4.4 Comparison with previous researches 47 4.5 Evaluation 48 4.5.1 Meteorological factors affect the forecasting result 51 4.5.2 The distance to trained dataset location affect the forecasting result 53 4.5.3 The optimized DNN models have equivalent forecasting results 54 4.6 Discussion 55 Chapter 5 Conclusion 58 References 60

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