Janitorial Rapid Response Predictive System – URANO

(V.D.02) New Technologies, (V.D.06) Clean Air

Climate change directly affects people’s lives, especially the most vulnerable who live in big cities. PlanClima (Climate Action Plan of the Municipality of São Paulo 2020 – 2050) is the main institutional guideline for the theme and brings several mitigation and adaptation actions for the municipality. This predictive system is in line with these guidelines, contemplating the objective of “minimizing flooding;” and subsidizes Action 29 – “Strengthening the governance of the Municipal Civil Defense, through the structuring, implementation, and monitoring of the Detection and Early Warning System for Civil Defense Risks”. This document brings together a brief history of the concept and presentation in practice of predictive analytics through machine learning integrated into urban janitorial processes, as well as the impact and added value of the Smart City for the Municipality of São Paulo, which aims to improve public management planning with technological solutions and provide a better quality of life for the population in the face of climate events.

Machine learning is a form of predictive analytics that moves organizations forward on the business intelligence (BI) maturity curve. no machine learning – a branch of artificial intelligence (AI) – systems are “trained” to use specialized algorithms to study, learn, and make predictions and recommendations to produce increasingly reliable and repeatable decisions and results. Over time, this interaction makes the system “smarter ” and increasingly able to discover hidden insights, relationships, and historical trends in order to predict critical situations. Thus, the application of predictive analysis using machine learning in urban janitorial processes enables a better understanding and response to weather events. This allows public management to anticipate problems, implement preventive measures more efficiently, and make decisions based on reliable data and information. As a result, the city of São Paulo can benefit from more agile management, the reduction of damage caused by adverse weather events, and, consequently, the improvement in the quality of life of its citizens.

Official Name of Signatory

City of Sao Paulo

Delegation

Brazil

Website of the Signatory

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Name of the person presenting the Good Practice

Lucas Roberto Paredes Santos

Position/Job Title of person presenting the Good Practice

Assessor International Relations

Aim of the Good Practice

Innovate the management of preventive and operational urban janitorial actions in a quick and planned way, restoring the mobility of the city; Produce information for decision makers about the general situation in which the city is in relation to the drainage system and its equipment (pools and polders), in addition to predictive analysis of the probability of occurrences related to precipitation (rainfall) and gusts of wind inside of the municipal territory; Articulate the quick and operational execution of preventive interventions in the drainage network, such as cleaning streams, galleries, manholes, manholes, pools and city ponds, in addition to carrying out necessary maintenance services and extremely important for this entire system to be able to capture and retain rainwater and thus avoid or reduce the impacts of these events on residents; Indicate the degree of probability (low-medium-high) of occurrences of road flooding events, overflowing rivers and streams, landslides and falling trees in districts and subprefectures under certain risk relative to weather conditions at a given time in the city; Conduct the fast and objective flow of communication with agents in the field in order to establish protocols for decision-making and actions in eventual critical situations within each line of information provided by the system; Control the equipment, galleries and drainage channels that had the most volume of rainwater runoff in a given period, directing the tasks and operations related to cleaning and maintenance of the city's drainage complexes. Articulate janitorial actions in order to reduce the city's social (safeguarding lives), environmental and economic vulnerability; Integrate operating protocols to be adopted and disseminated to the organized Subprefectures; Carry out the fulfillment of the attributions of SMSUB and Subprefectures within the janitorial actions carried out during the period of validity of the Preventive Plan Rains of Summer of the city of São Paulo; Reduce urban janitorial operating costs.

Target Group of the Good Practice

Administrative body of municipal public management, companies providing public services and all citizens and non-resident workers in the city's territory and, above all, the most vulnerable communities found in risk areas and precarious settlements.

Implementation period

Implemented in 2020, the URANO Predictive Rapid Response Janitorial System had its testing and adjustment phase in the summer (2020/2021) and has since been improved based on the machine concept learning .

Consistency over time

For SMSUB and Subprefectures, the URANO Janitorial Rapid Response Predictive System It is essential during and, above all, after the end of the summer rainy season. It is precisely after the end of the rainy season that all the preventive and maintenance work carried out by the subprefectures gains intensity in order to prepare the city for facing the next rainy season. In this context, the URANO Predictive Rapid Response Janitorial System comes in as a great conductor in directing and managing all the actions carried out, in addition to systematizing and measuring everything that was performed, allowing the rapid and objective development of reports that express the preventive/predictive work of SMSUB and Subprefectures. This makes it clear that it is possible to innovate through artificial intelligence, which is already a reality in the world's big cities.

Evaluation of the Good Practice

URANO Predictive Rapid Response Janitorial System is an innovative system in the State of São Paulo that, through artificial intelligence, integrates data and processes them, having as output a series of predictive information about the action of rainfall on the municipal territory. The information is directed to the respective subprefectures through the Operational Control Center of the Subprefectures Department (CCO/SMSUB) and due to the predictive and quick nature of this information, duly organized and objective, SMSUB and the Subprefectures enable the planning, execution and management of actions janitorial services necessary to reduce the consequences of flooding, landslides and falling trees in the city, within its attributions expressed in the Ordinance that formalizes the Preventive Plan for Summer Rains in the city of São Paulo – PPCV. The experiences in the use and calibration of the system allow us to state that the use of its technologies and information is feasible and practical. In a quick and objective way, the system enables greater efficiency, rationality and cost reduction in the execution of janitorial actions, whether preventive, maintenance or operational.

Key stakeholders and partnerships

São Paulo City Hall (PMSP); Department of Subprefectures (SMSUB); Sub Prefectures of the Municipality of São Paulo; Developer partner companies; Other Management Departments of the Municipality of São Paulo; Non-resident citizens and workers in the city of São Paulo.

Link for more information

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