About this project
Project Abstract: Automated Weather Prediction System for Disaster Risk Reduction
Climate-related disasters such as floods, droughts, and storms are increasingly affecting vulnerable communities, especially in developing regions. To address this challenge, our project introduces an Automated Weather Prediction System that leverages real-time data collection, AI-powered forecasting models, and integrated alert mechanisms to enhance disaster preparedness and resilience.
The system collects meteorological data through IoT-enabled weather sensors and satellite feeds, which are then processed using machine learning algorithms to generate accurate short-term and long-term weather forecasts. These forecasts are automatically disseminated to local authorities, schools, farmers, and the general public via SMS, mobile apps, radio, and community alert systems. The platform also supports geospatial risk mapping and early warning protocols, empowering stakeholders to make informed decisions and take preventive actions.
By providing timely and localized weather intelligence, the system contributes directly to Disaster Risk Reduction (DRR) by reducing human and economic losses, improving emergency response, and strengthening climate adaptation strategies. This innovation aligns with the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals, particularly SDG 13 (Climate Action) and SDG 11 (Sustainable Cities and Communities).
Goals and Objectives
Project Goal:
To enhance community resilience and reduce the impact of climate-related disasters through an automated, accurate, and accessible weather prediction and early warning system.
Project Objectives:
Develop and deploy an automated weather prediction system using real-time data collection, IoT sensors, and AI-based forecasting models.
Improve early warning capabilities by integrating the system with multi-channel alert mechanisms (SMS, mobile app, radio, sirens) to reach vulnerable communities in real time.
Strengthen disaster preparedness by providing localized and actionable weather forecasts to decision-makers, schools, farmers, health facilities, and emergency responders.
Support data-driven disaster response planning by generating predictive risk maps and analytics dashboards to monitor weather trends and potential hazards.
Promote community awareness and education on interpreting weather forecasts and responding to warnings through workshops, school programs, and public outreach.
Contribute to national and regional disaster risk reduction strategies, aligning with the Sendai Framework and national climate resilience goals.
Ensure inclusivity and accessibility of the system, especially for women, youth, persons with disabilities, and people in remote or underserved areas.
Expected result
Expected Results (SMART)
Improved Forecast Accuracy by 80% within 6 Months
Through the integration of IoT-enabled sensors, satellite data, and AI-based modeling, the system is expected to produce weather forecasts with an accuracy level of at least 80% for localized weather events (e.g., rainfall, temperature, wind). These forecasts will be validated against historical weather patterns and Kenya Meteorological Department data, allowing continuous system calibration and improvement.
Reach 5,000+ People with Early Warnings in the First Year
The system will deploy a multi-channel early warning mechanism (SMS alerts, mobile app push notifications, community radio broadcasts, and sirens). Within the first 12 months, we aim to reach at least 5,000 people in disaster-prone areas, with a minimum of 60% being women and youth. Monitoring tools like SMS delivery logs, app usage metrics, and field surveys will be used to track outreach.
Reduce Disaster-Related Response Time by 40% in Target Communities within 1 Year
By enabling early detection of floods, storms, and extreme temperature events, the system will reduce the average emergency response time by local authorities and communities. Baseline response time data will be collected during the first three months, and improvements will be monitored quarterly through simulation drills and disaster reports.
Establish 10 Community Weather Info Points within 9 Months
To ensure equitable access to forecasts and alerts, especially in rural and offline communities, we will install 10 physical display boards and siren-equipped weather info points in strategic community locations. Each site will be monitored weekly, and community feedback will be collected monthly.
Train 200+ Stakeholders on Weather Literacy and Risk Response by Month 12
Workshops and school outreach programs will train local leaders, students, farmers, and disaster response teams on interpreting weather data and responding appropriately to alerts. A minimum of 40% of trainees will be women and youth. Pre- and post-training assessments will measure knowledge gains and retention.
Enable 24/7 Real-Time Monitoring of Weather Data Starting Month 3
The system will begin real-time data collection from sensors and satellites by the third month, enabling continuous monitoring of weather indicators. This data will be accessible via a dashboard for disaster agencies and periodically backed up for analysis and research. Monitoring will be audited monthly for uptime and data reliability.
Document and Share Project Impact and Lessons Learned by Month 12
A comprehensive monitoring and evaluation framework will be used to track all activities and results. A final report detailing impacts, community feedback, challenges, and scalability recommendations will be published and shared with stakeholders, partners, and local authorities.
About me / organisation
PHANICE ACHIENG
About the Project Leader
Name: Phanice Achieng
Age: 18
Role: Project Lead – Automated Weather Prediction System for Disaster Risk Reduction
Phanice Achieng is a passionate STEM innovator, researcher, and youth leader dedicated to leveraging technology for social good, climate resilience, and inclusive development. She is currently studying at Kaimosi girls high school, with extensive practical experience in environmental sensing, artificial intelligence, and disaster preparedness.
Phanice has led and contributed to multiple award-winning innovations, including AquaGuard Flood Protection System, and is the founder of several socially impactful projects across education, accessibility, and environmental sustainability. As a Generation Connect Youth Envoy for the United Nations ITU,she has represented youth voices at global policy tables and contributed to shaping digital solutions for development.
She has served as a judge advisor for Technovation Girls, a reviewer for the Mandela Washington Fellowship, and a coordinator with Young Scientists Kenya, where he supports youth innovation projects. His experience includes community training, IoT prototyping, mobile app development, and data analysis—skills crucial to the successful implementation of this project.
As the project leader, Phanice oversees overall strategy, system design, team coordination, stakeholder engagement, and impact monitoring. Her strong academic background, hands-on innovation experience, and commitment to climate action make him exceptionally suited to lead this solution from idea to impact.