Question 1

How does AI waste prediction work?

Accepted Answer

EcoOS uses ensemble machine learning models (Random Forest, XGBoost, Neural Network, and LLM) to forecast food waste before meals are served. The system analyzes historical waste data, menu items, attendance figures, and day-of-week patterns to predict waste quantities with up to 94.8% accuracy.

Question 2

What data does EcoOS analyze?

Accepted Answer

EcoOS analyzes historical waste records, menu compositions, attendance counts, day-of-week patterns, seasonal trends, and intervention outcomes to generate accurate waste predictions and actionable recommendations.

Question 3

How accurate are the predictions?

Accepted Answer

Our ensemble ML models achieve 94.8% prediction accuracy across institutional food service operations. The multi-model portfolio lets you choose from five models to best match your operational profile.

Question 4

Is EcoOS available for Hong Kong operations?

Accepted Answer

Yes. EcoOS is optimized for Hong Kong institutional food service, using local waste statistics and Hong Kong Environmental Protection Department data. The platform addresses Hong Kong's 3,600 tonnes of daily food waste sent to landfills.

Question 5

What models are used for waste prediction?

Accepted Answer

EcoOS offers a five-model portfolio: Random Forest (RF), XGBoost, Neural Network (NN), Linear Regression (LR), and LLM-based prediction. Each model can be selected based on your specific accuracy and interpretability needs.