A Deep learning software that analyzes different variables in the environment to predict salmon spawning and educate students
Thomas Yuan
St George's School
Floor Location : M 055 V

Right before summer, our school went to a remote town in British Columbia, Canada called Cheakamus, where we studied the environment. Cheakamus is dealing with some data problems that can be solved with computer engineering. In Cheakamus, students get to record data such as the river temperature, precipitation, and water. However, every year hundreds and thousands of students go to Cheakamus to learn and study, yet there is not a convenient way to learn the environmental data collection. More specifically, students collect lots of data but are not able to understand the relationship between different collected data. However, studying the salmon population is a highlight in Cheakamus. Many students enjoy this process, and genuinely care about the salmon population. Thus, it is crucial to know how to preserve the population or why it was not preserved. Through experiments, I found that the data that students collect is highly correlated with the salmon population. Therefore, a salmon forecasting software was created.

The software that is created to solve this problem is a statistical analysis software that uses machine learning to analyze the relationship between variables and predict salmon spawning. In the current market, there are many softwares that are designed to be utilized by companies with huge data sets. Due to the complexity of the statistical analysis softwares, these softwares are not useful to students who are trying to understand the environment. In addition, the current statistical analysis apps are not specific to analyzing and predicting the salmon population. Therefore, even if students could use the current statistical analysis software, they would not get an accurate prediction of salmon spawning.

The environmental statistical analysis software I'm creating differs from the previous statistical analysis applications. The interface consists of a csv file that includes variables of the
environment including river temperature, river level, and precipitation. On the other hand, the output of the software would be the amount of salmon spawning.

This software includes a database that is able to be compared with the data that is inputted by a user. This feature saves valuable time, because users normally have to enter data into their own database. The database contains variables such as temperature, precipitation, salmon population and river levels from previous years in the Chekamus region. When a user inputs the current temperature, precipitation, and river level. The software gives a prediction of the amount of salmon spawning. For example, we often say that the number of salmon is related to temperature, river level and precipitation. The database which contains the number of salmon and the other three variables could explain the significance of relationship between these variables.