Applications of Artificial Intelligence for Hourly Rainfall Forecast and Deception Detection

Autor: Ding-Wei Liu, 劉丁瑋
Rok vydání: 2017
Druh dokumentu: 學位論文 ; thesis
Popis: 105
Based on artificial intelligence deep learning has been applied on many applications. The advantage lies in its less development time consumption and relatively more simple than the dynamic or physical models. This study uses an easy, fast and efficient non-linear algorithm, Reservoir Computing ( RC ), based on Echo State Network ( ESN ) algorithm. We apply this algorithm for the prediction of rainfall and the deception detection. Precipitation is a useful information for assessing vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using deep learning algorithms are a promising way for these purposes. In this study, we used ESN to analyze the meteorological hourly data from 2002 to 2014 at the Tainan Zengwen Observatory (120.497E,23.219N) and the sea level station in Kaohsiung (120.283E, 22.617N). We also compared the prediction and observation by using the ESN algorithm and a commercial neuron network MATLAB toolbox. The results show that the ESN can provide a better performance to predict rainfall. Another subject of this study is the realization of deception detection based on deep learning. Nowadays, polygraph is performed by the observation of the subject's skin resistance, respiratory wave and pulse wave (blood pressure) to measure people's psychological changes. In this study, the vocal signal of the answer to the subjects is acquired. After the processing of the Mel-scale frequency coefficient (MFCC), which is commonly used in speech recognition and speaker recognition, the signal is treated by ESN. The result shows that the method applied on deception detection can also provide a good result. The average accuracy of the deception detection is 65%. The highest correctness can be up to 100%.
Databáze: Networked Digital Library of Theses & Dissertations
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