Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics
Room: Park Tower 8219
Xavier Garrido - University of Hawaii West Oahu
Co-Author(s): Esther Widiasih, University of Hawaii at West Oahu, Oahu, Hawaii
Dissolved oxygen is one of the most important aspects of aquaculture, as it is essential to fish growth and health. Oxygen levels below 5ppm cause stress on fish, and below 3ppm could be lethal. Loko ‘ia (Hawaiian fishpond) is one form of aquaculture, practiced for centuries on the island chain. In the past, elevated water temperature in combination with other factors such as weakening trade winds, have resulted in a rapid decrease of dissolved oxygen, causing large fish kills in some loko ‘ia around O’ahu. The amount of dissolved oxygen in the system is a function of many factors, including solar irradiance, windspeed, and temperature. Recently, dissolved oxygen and temperature sensors are placed in select loko i’a and the time series data is publicly available through smartcoastlines.org, including at Mokeauea fishpond in O’ahu. We hypothesized that using data from these sensors, one can predict a fish kill event at Mokauea loko ʻia. Autoregressive integrated moving average (ARIMA) is a statistical technique to analyze and forecast time series data. Using the programming language R, we apply ARIMA with input variables temperature from smartcoastlines.org as well as wind speed from NOAA. Preliminary results show that comparing observed data to the ARIMA forecast, an increase in temperature and a decrease in wind speed could predict a lowered level of dissolved oxygen. For this model to be useful to loko ʻia practitioners, a lead time of one week or less would be most desirable.
Funder Acknowledgement(s): Dynamics of Mokauea
Faculty Advisor: Esther Widiasih, email@example.com
Role: I created the R script that we used to analyze the time series data for the dissolved oxygen, and create figures. I also performed the task of data aggregation, and cleaning.