Cities produce large amounts of waste, from litter to sewage; managing this waste is a constant challenge for municipal authorities. The smart city model, then, involves the use of IoT technology and machine learning to run and to optimize a municipality’s waste management system.
AI-optimized waste management improves the efficiency of waste management systems while reducing costs. The resulting effects improve the health of citizens, especially of the waste management employees.
Not to be discounted is the effect of beautifying the city itself.
To build an effective waste management system, begin with the location and condition of all city garbage bins, sewage lines and tanks, and any other waste collection and transportation equipment. Also include the current waste collection schedules and history of overflows or increased collection needs.
Necessary external waste management data includes sensor data from garbage bones, septic tanks, and trucks. Event data also provides important information on the amount and type of waste a city can expect.
Weather and natural disaster data straddle the line between merely useful and essential since, depending on the city location, extreme weather and natural disasters cause regular waste management problems. For example, seasonal monsoons lead to sewer system flooding.
Traffic data can also help waste transportation vehicles to work without adding to city congestion.
Implementation of any type of waste management technology is very difficult and costly. The scale alone makes many balk at implementation. Many cities, therefore, adopt new technologies in a piece-meal fashion or outsource the collection of waste to private companies. However, this outsourcing removes one of the most basic public service from the public office, which leaves citizens with diminished ability to seek satisfaction from cities whenever they have complaints.
In addition, municipal bin sensors cannot measure garbage if citizens don’t bother to throw it in the bins; human laziness can defeat technology sometimes.
Guangdong-based firm Xiaohuanggou (XHG) has rolled out intelligent recycling bins, which are able to guide users to sort their household trash with the help of computer vision and auto calculations. The company also incentivizes users that put their garbage in the right place with cash rewards that can be exchanged for products on Xiaohuanggou’s app, online games, or even converted into donations to charities. The firm has set up facilities in over 40 cities across the country, including Chongqing, Beijing, Shanghai, Dongguan, and Guangzhou, according to its website.
Companies are also employing AI-assisted machines to sort garbage in recycling plants and waste disposal factories, alleviating human labor in dirty and hazardous garbage processing plants.
Table of INEBase Amount of non hazardous and hazardous waste managed by type of waste and type of treatment and type of hazardous. National. Statistics on the Collection and Treatment of Waste
Table of INEBase Amount of urban waste collected classified by type of waste and Autonomous Communities. Autonomous Communities. Statistics on the Collection and Treatment of Waste
Table of INEBase Amount of waste generated by economic activity CNAE-2009, type of waste and type of danger. National. Surveys on Waste Generation
Waste Regulation Management The National Transfrontier Waste Shipment Office in Dublin City Council is the single point of contact for national waste imports and exports, including the administration and enforcement of environmental regulations. This data is extracted from Dublin City Council’s Waste Regulation Management System (WRMS) tracking system for Waste Collection Permits, Waste Facility Permits, National Hazardous Waste Movement and Transfrontier Shipments of Waste. The two datasets include a list of authorised waste collectors and waste facility permits (permits, names and addresses). Data covers national areas. Permits available through EPA.
Irys on Cities and Government provides detailed geolocation maps, tools, and reports. on infrastructure, housing, and transportation problems