https://github.com/civicdatalab/idea-frm

Analytical and Machine Learning modelling for Assam's Flood Response and Management.
https://github.com/civicdatalab/idea-frm

Last synced: 12 months ago
JSON representation

Analytical and Machine Learning modelling for Assam's Flood Response and Management.

Host: GitHub
URL: https://github.com/civicdatalab/idea-frm
Owner: CivicDataLab
Created: 2022-10-18T09:29:07.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2022-11-30T05:47:42.000Z (about 3 years ago)
Last Synced: 2025-01-14T02:12:06.840Z (about 1 year ago)
Size: 48.7 MB
Stars: 2
Watchers: 8
Forks: 1
Open Issues: 4
Metadata Files:
- Readme: README.md
- Contributing: Contributing.md

Awesome Lists containing this project

README

# Intelligent Data Ecosystem for Assam - Flood Response & Management (IDEA-FRM)
IDEA-FRM combines different flood-related datasets such as the Satelite data, socioeconomic data, data on past damages and the fiscal data onto a single point, for decision-makers, experts, enterprises, academicians and interested citizens to understand Assam floods holistically and co-create impactful solutions.

## About Open Contracting India
Open Contracting India is an initiative by [CivicDataLab](https://civicdatalab.in/) in partnership with [Open Contracting Partnership](https://www.open-contracting.org/) that aims at making public procurement processes in India more efficient, accessible and participatory.

We do this by onboarding governments to international data standards for curating and releasing data, developing platforms to analyse their data for better decision-making, creating case studies and resource material to understand gaps and benefits of actions.

## Directory Tree:
1. [satellite_and_weather_data](https://github.com/CivicDataLab/IDEA-FRM/tree/main/satellite_and_weather_data): This salient data type informs the factors and extent of floods but can help in developing early warning systems using predictive modelling.
2. [demographic_data](https://github.com/CivicDataLab/IDEA-FRM/tree/main/demographic_data): To understand how floods interact with settlements and human lives, we measure demographic vulnerability by studying the population, sex ratio, deprived population, household access to drinking water and sanitation.
3. [access_to_infra](https://github.com/CivicDataLab/IDEA-FRM/tree/main/access_to_infra): This layer evaluates the proximity to physical infrastructure like roads, rail, hospitals and community shelters.
4. [past_damages](https://github.com/CivicDataLab/IDEA-FRM/tree/main/past_damages): The Assam State Disaster Management Authority [(ASDMA)](http://sdmassam.nic.in/) maintains a robust data system that collects information on damages due to floods in every monsoon season. This data includes population affected by floods, houses damaged, crop area affected, infrastructure affected etc.
5. [government_response](https://github.com/CivicDataLab/IDEA-FRM/tree/main/government_response): Government response before, during and after disaster is principal while proposing any solution. We are looking through the lens of such expenditure, as seen in [government procurement data](assamtenders.gov.in), budget data and relief distributed (as reported in FRIMS).

## Contributors: