Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine

Abstract

Abstract As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased. To recover quickly from flood damage and prevent further consequential damage, flood waste prediction is of utmost importance. Therefore, developing a rapid and accurate prediction of flood waste generation is important in order to reduce disaster. Several approaches of flood waste classification have been proposed by various researchers, however only a few focus on prediction of flood waste. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) approach is adapted to address these challenges. Two different raw datasets were obtained from the “Advancing Sustainable Materials Management: Facts and Figures 2015” source. The datasets were for 9 years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Municipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015. The waste types were grouped as paper and paperboard (PP), glass (GI), metals (Mt), plastics (PI), rubber and leather (RL), textiles (Tt), wood (Wd), food (Fd), yard trimmings (YT) and miscellaneous inorganic wastes (IW).

Keywords

• Flood waste
• Flood management
• Long short-term memory
• Prediction
• Deep learning