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Politics and people | How AI is helping India’s poor become climate resilient


Pooja Singh, a 35-year-old domestic helper, works nearly 12 hours a day to support her family of four. His income isn’t enough to cover the expenses, but Singh, a school dropout, has made a significant investment this year. The mother of two schoolchildren took out a loan of 15,000 from a relative to buy an insulation sheet to cover the ubiquitous blue tin roof of his house, located inside a congested slum in Ghaziabad, Uttar Pradesh.

“The summers are getting hotter every year, and my house is becoming a furnace. My elderly parents find it difficult to rest. I can’t sleep either after a hard day’s work. I got sick twice this year and employers cut my salary. My children cannot study. The insulation sheet is a godsend. It was worth the money and effort,” says Singh.

In a world hit by the climate crisis, people like Singh, who cannot afford cooling devices or are not responsible for global warming, are paying a heavy price for rising temperatures.

The rapid increase in heat gain from exposure to hotter than average conditions compromises the body’s ability to regulate temperature and can lead to a cascade of illnesses including heat cramps, heat exhaustion, heatstroke and hyperthermia, warns the World Health Organization. In addition, deaths and hospitalizations (another huge drain on already scarce resources) due to heat-related illnesses can occur exceptionally quickly (same day) or have a lagged effect (several days later) and lead to a acceleration of death or disease.

Global warming also increases work-related heat stress, which hurts productivity and leads to job and economic losses. The poorest countries will be the most affected, warns a 2019 report by the International Labor Organization.

Fight high temperatures

Several scientific reports have warned that heat waves will become frequent and intense in India (and around the world) in the coming years.

This year has been terrible.

A excluding tax analysis of the global temperature database maintained by NASA shows that the June-July-August period may well have been the warmest the world has seen since 1880.

Unfortunately, the discussion around the impact of heat on cities and their population does not include a very significant part of the population, the disadvantaged, who live in very congested slums, which have much higher temperatures (especially inside) than the surrounding areas.

“If the temperature in any area of ​​the city is 38°C, be sure it would be several degrees higher inside a tin shack due to the construction materials used and the lack of adequate ventilation,” says Dr. Anshu Sharma, co-founder of SEEDS. , a non-profit disaster response and preparedness organization based in New Delhi. “Such a temperature spike affects those who don’t usually go out to work such as women, young children, people with disabilities and the elderly.”

Unfortunately, the impact of heat waves is not discussed enough.

“There is a lack of data on heat-related deaths. Most deaths are not reported as heat stress deaths. Indeed, the person will eventually die of fever, cardiac arrest or dehydration. So without granular data, generating a conversation about heat and its severe impacts is very difficult,” adds Sharma.

No localized information

Although the government regularly issues heat advisories, they are not localized and easily decipherable by at-risk communities and other actors involved in disaster response and planning.

To overcome this challenge, SEEDS, together with Microsoft and its technology partner Gramener, has developed “Sunny Lives”, an AI model that bridges this gap so that communities can be better prepared and governments can better plan ahead. local scale.

The model, developed as part of Microsoft’s global AI for Humanitarian Action program, processes large volumes of data to provide risk insights at a hyper-local (building group) level.

The model generated heat wave risk information for about 125,000 people living in slums in New Delhi and Nagpur. (Screenshots: SaferWorldComm/Youtube)

“The condition of a building’s roof (for example, tin foil heats up much faster than other materials) says a lot about the condition inside the house. The AI ​​model uses high-resolution satellite imagery to detect and assign risk scores to buildings based on their roof types,” says Sharma.

Other location- and hazard-specific attributes – built density, vegetation, the building’s proximity to a body of water, and roof material, with its heat absorption capacity – are also used to calculate hazard scores. houses. Then these structures are identified, classified, color coded and mapped with their risk score.

The risk map is then overlaid on a regular map available on a smartphone, which the volunteers then use in the field. This helps them determine where they should go to issue warnings, the risks of water shortages in an area, or where local administrators should direct resources.

The model generated heat wave risk information for about 125,000 people living in slums in New Delhi and Nagpur.

reach people

However, generating this granular data and mapping homes is not enough.

SEEDS’ biggest challenge is communicating the dangers of a heat wave because, unlike other hazards – floods, earthquakes or cyclones – the impact of a heat wave is not exactly visible. Also, people should know how to react to these warnings and be informed about inexpensive solutions such as painting the roof white, growing vegetables on the roof, or putting insulation under the roof to reduce the temperature outside. interior of rooms.

To ensure people understand the risk scores and encourage them to adopt appropriate climate resilience strategies, SEED runs focused panel discussions and heat quizzes.

“These campaigns are designed to understand people’s perception of heat, the challenges and concerns faced by communities, and the actions and interventions taken by community members to address heat and its impacts,” Sharma explains. The organization is also interviewing frontline workers such as credentialed social health activists and Anganwadi workers to understand the impact of rising temperatures on the community.

During these discussions, the local populations also developed their strategies. For example, Razia, a domestic worker, put a few burlap sacks on her bamboo roof to insulate it. Vanshika, a student, covered her family’s upper water tank with polystyrene sheets and burlap sacks, which provided just enough insulation to keep the water from heating up.

The future of the AI ​​model

As the model matures, the model deployment will be scaled to multiple cities across the country.

“We are also looking to collaborate with various municipal and state governments, to help them develop risk-informed planning. Our vision for the model is to use it for climate change adaptation and disaster management in a way that communities’ hyper-local risk is understood and pathways to their protection and resilience are put into practice. says Sharma.

Opinions expressed are personal

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    KumKum Dasgupta is in the opinion section of Hindustan Times. She writes on education, environment, gender, urbanization and civil society. .
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