基於深度學習技術用於預測海平面高度之變化

Application of Deep Learning Technology to Predict Changes in Sea Level

根據世界氣象組織的數據顯示，地球上的氣溫從1850年至2020年已升高將近1℃，造成全球氣候異常，嚴重影響南極冰層與格陵蘭冰層融化。若在未來100年內，冰層全部完全溶化，海平面就會上升67.2米，大部份沿海城市都會沉沒於海中，四面環海的島嶼國家有可能從此消失。本研究以預測海平面升高之高度為主要探討，由歷年的二氧化碳推算出全球溫度、冰層面積，預測未來海平面將會升高多少。本研究利用全球歷年二氧化碳、全球歷年氣溫、全球歷年北極海冰面積、全球歷年海平面高度資料進行分析，並使用線性回歸與長短期記憶網路交互預測。首先使用線性回歸個別觀察每個數據的指數關係，再使用長短期記憶網路訓練互相有關係的數據，最後使用未來的年份當作預測值，推算每一個未來數值並預測未來30年或50年的海平面高度。

According to data from the World Meteorological Organization, the temperature on Earth has risen by nearly 1°C from 1850 to 2020, causing global climate anomalies and seriously affecting the melting of Antarctic ice and Greenland ice. In the next 100 years, if the ice completely melts, the sea level will rise by 67.2 meters. As a result, most coastal cities will be submerged in the sea, and the island nation surrounded by the sea may disappear. This study focuses on predicting the height of sea level rise, calculates global temperature and ice area from carbon dioxide over the years, and predicts how much sea level will rise in the future. This study uses global historical carbon dioxide, global temperature, global Arctic sea ice extent, global historical sea level data for analysis, and uses linear regression and long short-term memory network interactive prediction. First, this study uses linear regression to individually observe the exponential relationship of each data. Second, this study uses long short-term memory networks to train data that are related to each other. Finally, this study uses future years as forecast values, extrapolates each future value and predicts sea level heights for the next 30 or 50 years.