Computer Prediction of Seawater Sensor Parameters in the Central Arctic Region Based on Hybrid Machine Learning Algorithms

Yuchen Wang,Yinke Dou,Ce Liang,Yanzhao Hao,Xiaomin Chang,Wangxiao Yang,Guangyu Zuo,Ruina Sun,Zhe Yang,Jingxue Guo,Jianlong Liu

doi:10.1109/access.2020.3038570

Abstract

In recent years, with the large-scale reduction of Arctic sea ice, the supplement of chlorophyll sensor data in seawater has become an essential part of environmental assessment. Accurately predicting the chlorophyll sensor data in seawater is of great significance to protect the Arctic marine ecological environment. A machine learning prediction method combined with wavelet transform is proposed. This process uses data from upper ocean observation buoys placed in the Arctic Ocean (A.O.) to predict the sensor analogue of chlorophyll-a (C.A.) in the upper ocean of the Arctic Ocean. Choose the best wavelet transform method and prevent the LSTM gradient from disappearing. A model combining SAE (stacked autoencoder) Bi (bidirectional) LSTM (long short-term memory) and wavelet transform is proposed. Experiments were conducted to compare the predictive performance of buoy data input as univariate at two different times and locations in the Arctic Ocean. The results show that compared with other models (such as LSTM), in the SAE Bi LSTM model, the data of the two sites have the highest prediction accuracy. The best wavelet transform methods are fourth-order four-layer and first-order four-layer. The observational data of the Chukchi Sea from 2018 to 2019 obtained the best prediction results. The root mean square error (RMSE) value is 0.02003 volts; the average absolute error (MAE) is 0.0841 volts. This research provides a new method for predicting the chlorophyll sensor parameters in the upper ocean through the sea ice buoy observed at a given point, which helps to improve the accuracy of the ocean sensor parameter prediction on the Arctic ice buoy.

Highlights

The Arctic is one of the earth's cold sources, and its shape changes more obviously during the seasonal changes
The past decade has seen substantial advances in understanding Arctic amplification - that trends and variability in surface air temperature tend to be more significant in the Arctic region than for the northern hemisphere or globe as a whole.[2]
The conclusions imply that novel models have high-precision results to forecast compared with the linear model The final experimental result was that the root mean square error (RMSE) range was approximately 0.10 to 0.18, and the R2 field was about 0.50 to 0.80

Summary

Introduction

The Arctic is one of the earth's cold sources, and its shape changes more obviously during the seasonal changes. As the A.O. is increasingly altered by anthropogenic climate change, it is critical that we accurately assess ongoing changes in its capacity to support marine life.[1]. Other environmental parameters play an essential role in the process of Arctic changes, such as C.A. and dissolved oxygen in seawater[3]. Monitoring ocean C.A. content provides a tool to achieve a deeper understanding of the contribution of CO2 to the climate[4, 5]. In the observation of ice buoys in the A.O., it is challenging to obtain interannual data from large ice buoys (especially ice buoy data including ocean profile observations) due to equipment damage caused by sea ice compression or polar bear destruction. The environmental parameters of the VOLUME XX, 2017

Objectives

Results

Conclusion