AI-powered sanitation management system for refugee communities
/The Need
OXFAM Bangladesh, working in UNHCR refugee camps, faced critical challenges in managing sanitation infrastructure for thousands of displaced people living in densely populated settlements. Fecal sludge management—the process of servicing and maintaining latrines—required constant monitoring to ensure public health and dignity for camp residents.
With over 15,000 latrines scattered across multiple camp locations and indirectly serving more than 10,000 camp residents, OXFAM needed a sophisticated system to track servicing schedules, predict maintenance needs, and optimize desludging operations. The challenge was to move beyond reactive, manual management to a proactive, data-driven approach that could forecast which latrines would need servicing based on usage patterns and historical data.
OXFAM required a digital monitoring and prediction system that combined mobile-based data collection for field teams with AI-powered analytics that could optimize resource allocation, prevent overflow situations, and ensure every latrine received timely maintenance—all while operating reliably in the challenging infrastructure environment of refugee camps.
The Solution
We built a comprehensive digital monitoring and prediction system that revolutionized how OXFAM manages fecal sludge operations, transforming sanitation management from reactive crisis response to proactive, predictive maintenance.
The Smart Fecal Sludge Management system was built specifically for OXFAM Bangladesh to digitize and optimize desludging operations across UNHCR refugee camps. The platform centralizes all latrine data, tracks servicing history, monitors current status, and schedules maintenance activities.
Field teams can access up-to-date information about which latrines require attention, track completion of servicing tasks, and report issues in real-time. This systematic approach ensures no latrines are overlooked while preventing wasteful over-servicing of facilities that don't yet need attention.
The AI-Powered Prediction Engine represents a breakthrough innovation in humanitarian sanitation management. Our custom spatial AI model forecasts latrine servicing needs using live mobile data collected by field teams and historical desludging records.
The AI analyzes patterns including usage intensity based on nearby population density, time since last servicing, seasonal variations, and camp-specific factors to predict when each latrine will require desludging.
This predictive capability enables OXFAM to proactively schedule maintenance before problems occur, optimize routing for desludging trucks to service multiple nearby latrines efficiently, and allocate resources based on actual need rather than fixed schedules or reactive responses to overflows.
The Mobile + Web-Based Monitoring solution combines a Flutter-based Android app for field use with a Laravel/MySQL admin panel featuring comprehensive dashboards, alerts, and performance reports.
Field workers use the mobile app to record desludging activities, report latrine conditions, capture photos documenting maintenance, and receive notifications about scheduled servicing tasks—all while working offline in areas with limited connectivity.
The web-based admin panel provides OXFAM managers with real-time dashboards showing overall sanitation coverage, alerts for latrines requiring urgent attention, performance metrics tracking team productivity and service quality, and comprehensive reports for donor accountability and operational planning.
The measurable impact has been substantial: the system now monitors 15,000+ latrines remotely and indirectly serves more than 10,000 camp residents, ensuring consistent sanitation services that protect public health and preserve dignity for some of the world's most vulnerable populations.
The Challenge
The primary challenge was developing an AI prediction engine that could accurately forecast latrine servicing needs in the complex, dynamic environment of refugee camps.
Unlike residential sanitation where usage patterns are relatively stable, refugee camp latrines experience highly variable usage based on population density fluctuations, new arrivals, relocations, and seasonal factors.
Training a spatial AI model required collecting and analyzing historical desludging data, integrating live mobile data from field operations, and accounting for geographic factors like latrine proximity to living areas and accessibility for desludging vehicles.
Building a mobile application that could function reliably in refugee camps with limited or intermittent connectivity required sophisticated offline-first architecture.
Field workers needed to record desludging activities, access latrine information, and receive task assignments even without internet access, with the app intelligently synchronizing data when connectivity was restored.
Ensuring data integrity during offline operations and preventing conflicts when multiple field teams synchronized data simultaneously required careful technical design.
Creating a system that could monitor 15,000+ latrines across dispersed camp locations while maintaining real-time visibility and generating timely alerts required scalable data architecture and efficient query optimization.
The platform needed to process incoming field data, run AI predictions continuously as new information became available, and update dashboards and alerts without lag.
Designing user experiences appropriate for field workers with varying levels of digital literacy, working in challenging conditions, required extensive user research and iterative testing.
The mobile interface needed to be simple enough for quick data entry during desludging operations while capturing all the information required for accurate AI predictions and accountability reporting.
Integrating spatial prediction models into the operational platform required close collaboration between data scientists developing the AI algorithms and software engineers building the application infrastructure, ensuring predictions were not just academically accurate but operationally actionable and seamlessly integrated into field workflows.
The Partnership
Our collaboration with OXFAM Bangladesh was driven by a shared commitment to ensuring dignified living conditions for refugee communities through innovative sanitation management.
We worked closely with OXFAM's WASH (Water, Sanitation, and Hygiene) specialists, field supervisors, and desludging teams to understand the operational realities of maintaining sanitation infrastructure in refugee camps.
The partnership involved extensive field engagement in UNHCR camps to observe desludging operations, understand existing workflows, identify bottlenecks in manual tracking systems, and design digital solutions that would genuinely improve rather than complicate field operations.
We consulted with field workers who would ultimately use the mobile app to ensure it aligned with how they actually work.
We collaborated with OXFAM's data team to develop the AI prediction engine, analyzing historical desludging records to identify patterns and validate model accuracy against real-world outcomes.
This iterative development ensured the predictions would be reliable enough to base operational decisions on, rather than just theoretical exercises.
Our team delivered comprehensive services including data migration from legacy tracking systems, system architecture design balancing functionality with the connectivity constraints of refugee camps, UI design optimized for field conditions, and integration of spatial prediction models into the operational platform.
We also provided training for field teams on using the mobile app and for OXFAM managers on interpreting dashboards and leveraging AI predictions for operational planning.
The measurable success—monitoring 15,000+ latrines and serving 10,000+ camp residents—demonstrates how this collaborative approach created a system that leverages cutting-edge AI technology to solve one of humanitarian response's most challenging and underdiscussed problems, ensuring refugee communities have access to the sanitation services essential for health and dignity.
The Tech Stack
We developed the solution using Flutter for the Android mobile app and PHP Laravel with MySQL for the web platform. Flutter delivers a high-performance, native-quality mobile application that field workers use for data collection, with robust offline capabilities essential for operating in refugee camps with limited connectivity.
Laravel provides the backend framework for managing latrine data, desludging records, user authentication, and API services that support both mobile and web clients. MySQL ensures reliable data management for 15,000+ latrine records, historical desludging data, and user information with the performance needed for real-time dashboards and analytics.
The platform integrates custom spatial AI models developed specifically for predicting latrine servicing needs, using machine learning algorithms that analyze live mobile data and historical records to generate actionable forecasts.
Our comprehensive services included data migration from OXFAM's legacy tracking systems, system architecture optimized for the refugee camp environment, UI design tailored for field workers operating in challenging conditions, and integration of spatial prediction models into operational workflows—all working together to provide the smart fecal sludge management, AI-powered prediction engine, and mobile + web-based monitoring that enable OXFAM to proactively manage sanitation infrastructure serving thousands of refugees.
