発表概要：Forecasting household waste generation using conventional methods can present challenges due to the substantial variability and uncertainty in the process. Furthermore, previous studies focused on forecasting household waste generation at municipal or national levels may not be directly applicable to on-site waste collection processes. The objective of this research is to attain daily-level predictions of household waste generation and assess the advantages of using a leading-edge deep learning approach over conventional methods. We applied Multi-variable Long Short-Term Memory (LSTM) neural network utilizing a granular garbage disposal database for forecasting. This database is curated from a garbage disposal sensing platform currently operational in three cities within the Kanagawa prefecture, Japan, with plan for operation until 2025. Additionally, relevant web applications based on findings from this research will be developed for data visualization and routine optimization.