Over the past few years, global warming has had increasingly noticeable effects, especially through the melting of the polar ice caps. This has caused sea levels to rise, which puts coastal cities and islands at risk of flooding. To combat this issue, monitoring and examining water changes has proven effective in predicting natural disasters caused by rising sea levels. One crucial factor in understanding the impact of global warming is sea surface height (SSH). Measuring SSH can provide valuable information about ocean-level changes. This research used data from the Jason 2 altimetry radar satellite, which provided 36 cycle periods per year, to investigate water heights around the Hawaiian Islands in 2019. To accurately evaluate water height variations, a specific area near the Pacific Ocean close to the Hawaiian Islands was selected. By processing the collected satellite data, a water height chart was created, which revealed an overall increase in height over one year. This analysis provided insight into changing ocean levels in the region, highlighting the urgency of addressing potential threats faced by coastal communities. The study also explored several influential factors contributing to water height variations, such as temperature, precipitation, air pressure, and humidity in Google Earth engine cloud-based platform. Machine learning algorithms, including MLPR and XGBOOST, were used to model water height within the specified range. The results showed that the XGBOOST algorithm was superior in accurately predicting water height, with an impressive R-square value of 0.95. In comparison, the MLPR algorithm achieved an R-square value of 0.91. These findings underscore the effectiveness of using advanced machine learning techniques to understand and model the complex dynamics of water height fluctuations in response to climate change factors. By utilizing these insights, policymakers, scientists, and local authorities can make informed decisions and develop resilient strategies to mitigate the risks associated with rising sea levels. Such proactive measures are crucial for safeguarding vulnerable coastal cities and islands from the increasing frequency and severity of natural disasters exacerbated by global warming.
It's important to acknowledge that predicting the impact of global warming on water height involves inherent uncertainties. Machine learning algorithms can also be utilized to assess and quantify the uncertainty associated with the predictions. Techniques such as ensemble models, Monte Carlo simulations, or bootstrapping can help estimate the uncertainty intervals and provide a range of possible outcomes.
