The Early Warning System (EWS) Project
To Save lives of those at risk due to climate change.
Risk Of Flood By Avalanche
Among the worst-hit victims of global climate change are the communities in South and Central Asia’s high mountain regions. The frequent, intense avalanches kill countless people, plunging the survivors into the cycle of deeper perpetual poverty.
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Avalanche Deaths Are On A Record Pace This Season – Forbes.
Madad is helping the worst-hit victims of the world.
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Our Progress
About The Early Warning System (EWS) Project
We developed low-cost automated weather stations and a predictive AI model to issue timely community alerts and improve anticipatory avalanche risk management.
Madad, which means “help” in Persian is a aid and relief initiative to save lives of poverty-stricken communities. We know well what it means to live in an environment lacking reliable support and infrastructure.
Problem Statement
High mountain regions in Central Asia, Karakoram, Hindu-Kush, and the Himalayas are prone to snow avalanches. They witness catastrophic events resulting in the loss of lives and properties in the winter and spring seasons. There are over 600 villages in geographies where the Aga Khan Agency for Habitat (AKAH) operates in Afghanistan, Pakistan, and Tajikistan. The frequency and intensity of snow avalanche event are expected to increase due to atmospheric warming. The lack of a local (village) level avalanche early warning system (EWS) poses a significant challenge in providing actionable forewarning to community members for timely action to avert disaster. Current available avalanche EWSs are either regional or very site-specific.
Our Solution
Due to inadequate spatial resolution, physical model-based forecast systems, dependent on satellite-derived weather parameters, are not appropriate for local (village) level forewarning. An alternative is to deploy a statistical model using a self-learning algorithm to determine warning and danger thresholds; this algorithm would then communicate an alert to the community members and volunteers. Often, the avalanche-prone villages are in remote mountain geographies with no internet connectivity. Thus, this solution will be a standalone system to run the algorithm based on historical and near real-time weather and snow data (explanatory variable), as well as avalanche incident data (predicted variable). Community volunteers will feed the near real-time data to the algorithm.
Early Warning System
A self-learning predictive algorithm using historical weather (temperature, rainfall) and snow data (snowfall, snow depth), and avalanche event data, will be loaded into a microprocessor (Arduino), which will calculate the need for an avalanche warning and re-calibrate every time new data is ingested.
Alert/Warning Decision
Community volunteers will ingest local daily weather data (temperature, rainfall), and snow data (snowfall, snow depth), and avalanche events when recorded through a data input interface. The weather and snow data will be evaluated based on the pre-estimated trigger threshold, and accordingly, the algorithm makes a decision to disseminate alert/warning.
ย Dessemination
The alert/warning will be disseminated through different means: a display panel (with color bulbs) and a siren. Because our system is AI-based, it learns with every new event; because it is low-cost and can be implemented quickly, it will save lives, livelihoods, and property and prevent further poverty.
Our Team
The Early wanring System (EWS) Project
Hamida Babool
Founder
Location
Carrollton, Tx USA
Deo Raj Gurung
Co-Founder
Location
Dushanbe, Tajikistan
Akbar Thobani
Data Science Team Lead
Location
Carrollton, TX 75010
Shakeel Merchant
Instrument Team Lead
Location
Atlanta, GA USA
Douglas Chabot
Avalanche Advisor
Location
Bozeman, MT USA
Dr Salman Bhai, MD
NeuroNext ESI
Location
Dallas, TX, USA
Ron Simenhois
Avalanche AI Prediction Advisor
Location
Leadville, CO USA
Sarthak Ojha
Data Science
Team Member
Location
Mumbai, India
Mirza Samnani
Weather Station & Remote Sensing Specialist
Location
Atlanta, GA USA
Rahim Dobariya
GIS & Remote
Sensing
Location
Mumbai, India
Kaamil Thobani
Data Science Team
Intern
Location
Carrollton, TX USA
Suleman Punjani
Digital Marketing, Volunteer
Location
Lawrenceville, GA USA
Murad Samnani
Web MASTER, Volunteer
Location
Vancouver, Canada
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Recent Update
We are excited to share an update on Madad’s avalanche early warning system. In August, our team travelled to Khorog, Tajikistan, and successfully installed two low-cost weather monitoring stations that our engineers built. We are already receiving weather-related data from the stations. Our team will monitor the stations for the next few months, make necessary adjustments, and install 55 weather stations in 2023. These stations’ data will help issue timely early warnings to vulnerable communities when the avalanche threat is imminent.
Madad’s collaboration with Aga Khan Agency for Habitat (AKAH) is a step forward to mitigating the climate change impact on the mountain communities of Afghanistan, Tajikistan, and Pakistan.
We are very thankful to our team Onno Ruhl,ย Nusrat Nasab,ย Shodmon Hojibekov,ย Deo Raj Gurung,ย Shakeel Merchant,ย Akbar Thobani, @Doug Chabot,ย Salman Bhai, Mirza S.,ย Suleman Punjani,ย Murad Samnani,ย Kaamil Thobani,ย Rahim Dobariya,ย Saima Dhanani, CPA,ย Ron Simenhois, andย Harvard Innovation Labs.
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Use Of Our Technology
A robust avalanche Early Warning System (EWS) would help communities manage the risk of potential hazards effectively and make these communities more resilient to natural disasters. An EWS consists of four essential elements: risk knowledge, monitoring, dissemination and communication, and response capacity (Sufri et al., 2020). Community engagement across all four components is crucial to saving lives and minimizing environmental and economic damage associated with disaster events (Sufri et al., 2020).
AKAH has a Village Disaster Management Plan (VDMP) developed for every village where clear response protocols are prepared in case of any disaster. The AKAH Community Emergency Response Team (CERT) is mobilized to implement response protocols such as evacuation in case of any forewarning. The community members are advised not to venture out into avalanche-prone areas.ย
Thus, an alert will help community members refrain from venturing out in high avalanche risk and timely mobilize AKAH CERT for action if required.ย ย