Resources › CA MCQ Practice
7 Jul 2026SCIENCE & TECHNOLOGY3 questions

How AI Helped Maharashtra Stay Ahead of the Nashik Cloudburst

UPSC-standard MCQs with explanations, trap analysis, and approach guide. Answer after the test — not before.

1

Easy

1

Medium

1

Hard

Practice this set

3 questions · full analysis after submission · no sign-up required

Start test →Read the article first

Article summary

Maharashtra's disaster management machinery used the Bharat Forecast System (BharatFS) — India's next-generation AI-powered, high-resolution weather forecasting platform — to receive hyper-localised advance warnings of cloudburst-like rainfall over Nashik district, enabling proactive evacuation and preparedness measures. BharatFS represents a significant upgrade over conventional numerical weather prediction models by integrating machine learning with high-density observational data to generate district- and block-level forecasts. India's vulnerability to extreme precipitation events — cloudbursts, flash floods, and urban inundation — has historically been compounded by the coarse spatial resolution of legacy forecasting tools, which could not distinguish rainfall intensity at sub-district scales. The deployment of BharatFS marks a convergence of India's digital public infrastructure ambitions with its disaster risk reduction commitments under the Sendai Framework 2015–2030. For UPSC aspirants, this event sits at the intersection of GS3 topics — science and technology, disaster management, and internal security — and tests the candidate's ability to link technological innovation with governance outcomes.

What this tests

recallTests whether you read the article and retained key facts.
1Q
applicationTests whether you can apply the concept to a new scenario.
1Q
analysisTests whether you can reason across multiple related facts.
1Q

Sample questions — answers revealed after test

SCIENCE & TECHNOLOGYRecallEasy

Q1. Which of the following correctly describes the IMD definition of a 'cloudburst' and the ministry under which the Bharat Forecast System (BharatFS) was developed?

ARainfall of 100 mm or more per hour over a localised area; BharatFS developed under the Ministry of Earth Sciences
BRainfall of 64.5 mm or more per day over a localised area; BharatFS developed under the Ministry of Earth Sciences
CRainfall of 100 mm or more per hour over a localised area; BharatFS developed under the Ministry of Home Affairs
DRainfall of 100 mm or more per day over a localised area; BharatFS developed under the National Disaster Management Authority
Answer revealed after you submit the test
SCIENCE & TECHNOLOGYApplicationMedium

Q2. A State Disaster Management Authority receives a hyper-local flood warning generated by BharatFS six hours before an expected cloudburst in a mountainous district. The district collector asks her team to verify the source and act appropriately. Which of the following actions and characterisations of BharatFS would be INCORRECT guidance to the team?

ABharatFS uses machine learning inference on observational data, not a standalone physical-equation model, so the warning should be treated as probabilistic rather than deterministic.
BBharatFS being under the Ministry of Earth Sciences means the warning carries institutional legitimacy comparable to an IMD alert, and the SDMA's SOPs should be triggered.
CBharatFS is functionally equivalent to IFLOWS and DAMINI, so the district team should cross-verify with those systems to confirm the cloudburst prediction before acting.
DConventional NWP models may not have flagged this event because their 10–25 km grid resolution cannot capture a cloudburst confined to 20–30 sq km, making BharatFS's sub-district output uniquely valuable here.
Answer revealed after you submit the test
SCIENCE & TECHNOLOGYAnalysisHard

Q3. Consider the following statements regarding India's AI-powered weather forecasting ecosystem and its disaster governance architecture: 1. BharatFS is an AI inference layer built atop observational data and is functionally distinct from IMD's Doppler radar network, which remains a primary data-collection infrastructure. 2. The Sendai Framework for Disaster Risk Reduction 2015–2030, Target G, calls for all countries to establish a single national AI-based forecasting centre by 2030. 3. Under India's constitutional framework, disaster management is a State subject, yet NDMA under the Ministry of Home Affairs provides national policy coordination without requiring a constitutional amendment to do so. 4. Conventional NWP models such as GFS and ECMWF operate at grid resolutions of 10–25 km, making them structurally incapable of resolving cloudbursts that are typically confined to 20–30 sq km. Which of the above statements are correct?

A1 and 4 only
B1, 3 and 4 only
C2 and 3 only
D1, 2, 3 and 4
Answer revealed after you submit the test
Practice all 3 questions →All CA MCQ sets