Predicting Blossom Date of Cherry Tree With Support Vector Machine and Recurrent Neural Network

TL;DR

This paper explores using Support Vector Machines and Recurrent Neural Networks to accurately predict cherry blossom dates based on temperature data, aiding public planning and pollen season management.

Contribution

It introduces a novel application of SVC and LSTM models for predicting cherry blossom dates as a multiclass classification problem.

Findings

LSTM outperforms SVC in prediction accuracy

Models effectively forecast blossom dates based on temperature data

Approach aids public planning and pollen season prediction

Abstract

Our project probes the relationship between temperatures and the blossom date of cherry trees. Through modeling, future flowering will become predictive, helping the public plan travels and avoid pollen season. To predict the date when the cherry trees will blossom exactly could be viewed as a multiclass classification problem, so we applied the multi-class Support Vector Classifier (SVC) and Recurrent Neural Network (RNN), particularly Long Short-term Memory (LSTM), to formulate the problem. In the end, we evaluate and compare the performance of these approaches to find out which one might be more applicable in reality.