Sea Level Prediction Using Machine Learning

Abstract

Sea level prediction is essential for the design of coastal structures and harbor operations. This study presents a methodology to predict sea level changes using sea level height and meteorological factor observations at a tide gauge in Antalya Harbor, Turkey. To this end, two different scenarios were established to explore the most feasible input combinations for sea level prediction. These scenarios use lagged sea level observations (SC1), and both lagged sea level and meteorological factor observations (SC2) as the input for predictive modeling. Cross-correlation analysis was conducted to determine the optimum input combination for each scenario. Then, several predictive models were developed using linear regressions (MLR) and adaptive neuro-fuzzy inference system (ANFIS) techniques. The performance of the developed models was evaluated in terms of root mean squared error (RMSE), mean absolute error (MAE), scatter index (SI), and Nash Sutcliffe Efficiency (NSE) indices. The results showed that adding meteorological factors as input parameters increases the performance accuracy of the MLR models up to 33% for short-term sea level predictions. Moreover, the results contributed a more precise understanding that ANFIS is superior to MLR for sea level prediction using SC1- and SC2-based input combinations.

Phân tích chuỗi dữ liệu nghiệm triều sử dụng mạng nơ ron hồi tiếp với nút có cổng (GRU)

“…Kết quả cho thấy giá trị đạt được lần lượt là 6,97% và 0,049 m cho thấy mô hình có thể giảm thiểu một cách hiệu quả các vấn đề chồng chéo do phương pháp học máy gây ra trong dự báo liên tục [11]. Nghiên cứu [12] đã khảo sát hiệu quả của việc đưa thêm dữ liệu khí tượng khi sử dụng các mô hình MRL, ANFIS trong dự đoán mực nước. Kết quả thực nghiệm cho thấy, khi đưa thêm các yếu tố khí tượng làm tham số đầu vào sẽ tăng độ chính xác hiệu suất của mô hình MLR lên tới 33% để dự đoán mực nước biển ngắn hạn.…”

The Gulf of Mexico in trouble: Big data solutions to climate change science

“…Data-driving machine learning approach is used to predict the Loop Current evolution and the Loop Current ring formation in the Gulf of Mexico (Wang et al, 2019), including a forecast of the sea surface height of the Loop Current System (Zeng et al, 2015;Wang et al, 2021), and a forecast of velocity structures of the Loop Current and its eddies (Huang et al, 2021;Muhamed Ali et al, 2021;Huang et al, 2022b). All ocean basins are experiencing sea level rise and warming due to climate change and global warming, so predicting and understanding the sea-level rise is also done using the machine learning approaches (Roshni et al, 2019;Morovati et al, 2021;Nieves et al, 2021;Tur et al, 2021). Remote sensing data is quite helpful in detecting the oil spills by analyzing satellite images manually, however machine learning models can also help automate the detection and tracking of oil spills (Estes and Senger, 1971;Kubat et al, 1998;Shamsudeen, 2020).…”

Prediction of Sea Level in the Arabian Gulf Using Artificial Neural Networks

“…However, while harmonic analysis remains a reliable tool, its utility is contingent upon the availability of extensive sea level records over significant time spans. This requirement poses challenges in regions where comprehensive tidal measurements have not been effectively recorded [6].…”