調適性網路模糊推論系統預測海岸變化–以台灣宜蘭為例

Abstract

海岸侵蝕造成海岸線不斷的後退會引起土地流失也會造成住在海岸的居民生命的威脅及財產的損失，臺灣多高山平原少，可使用土地有限，又位於西太平洋颱風頻繁地區，現在又面臨全球氣候變遷，海平面會逐漸上升及沿岸的土地開發等等挑戰，海岸線的後退只會比以前更加嚴峻，而海岸的保護需要多年的規劃及施工，因此發展一套海岸變遷預測模式提早作預防的準備以保護海灘避免流失為當務課題，本文選擇台灣宜蘭為研究區域，從北而南分別蒐集外澳、大福、永鎮、蔀後、清水、利澤及新城等七個地區2004/1~20011/12每個月的灘線觀測資料，先以調合分析並採用F分配檢定，宜蘭海岸線的變化是否為週期變化，資料分析結果顯示宜蘭海岸在外澳、大福、永鎮、蔀後、清水及新城呈現年週期變化; 影響海岸線變化過程之主要受到地形變化、漂砂及波流三者營造力，不但個別機制複雜且彼此交互作用，使得海岸問題用物理模式解析十分困難，模擬結果依然存在不確定性，仍必須要有實測資料驗證;而近年類神經網路常用來模擬物理方程式難以描述之複雜非線性與時變性問題，被大量地應用在水文各領域預測上，本研究透過人工智慧相關技術建構海岸線變遷預測模式，探討調適性網路模糊推論系統(ANFIS)於海岸線變遷的合宜性，預測未來一年內灘線的變化可行性，根據資料分析結果顯示，本研究建構海岸線變遷預測模式，可以精確預測1年後，外澳、大福、永鎮、蔀後、清水、利澤及新城等七個地區的海岸線的變化，預測誤差均方根在1.12~5.37m之間，這結果足可提供海岸管理者作為未來海岸線規劃、管理及預警參考

Shoreline erosion is a worldwide problem that causes a major concern to the socio-economic developments in coastal cities for many countries. The increasingly intensive human activities along coasts enlarge coastal erosion areas and aggravate erosion processes, and thus cause land losses; moreover the global climate change in the past decades results in rising sea levels. Taiwan is frequently attacked by typhoons and shoreline erosion is a major concern to local residents. Shoreline change prediction has gained considerable attention; nevertheless, little consensus has been made on the best predictive methodology due to the complex heterogeneity of coastal geomorphology and sediment-transport processes. This study intends to model the shoreline change through investigating monthly shoreline position data collected from seven sandy beaches located at the Yilan County in Taiwan during 2004-2011. The harmonic analysis results indicate shorelines appear significantly periodic with great variation. The adaptive neuro-fuzzy inference system network (ANFIS) is configured with two scenarios, namely lumped and site specific, to extract significant features of shoreline changes for making shoreline position predictions in the next year. The lumped models for all stations are first investigated based on a number of possible input information, such as month, location, and the maximum and mean wave heights. The results, however, are not as favorable as expected, and wave heights do not contribute to modelling due to their high variability. Consequently, a site-specific model is constructed for each station, with its current position and nearby stations’ positions as model inputs, to predict its shoreline position in the next year. The results indicate that the constructed ANFIS models can accurately predict shoreline changes and can serve as a valuable tool for future coastline erosion warning and management.