Proactive Water Contamination Forecasting with Machine Learning for a Leading Environmental Tech Startup

Client Overview

Our client, a European leader in water treatment technology, is a highly innovative startup renowned for its award-winning solutions in environmental sustainability. With over 100 clients across 55 countries and a network of 10,000+ sensors, the company gathers vast volumes of data—approximately 1 million new observations per day—related to water quality. Their clients, including both government agencies and private enterprises, manage water reservoirs and lakes, often relying on the startup’s expertise to monitor contamination levels and recommend treatment actions. They install sensors at these locations and manually review the sensor data regularly to advise on the requirement and specification of water treatments.

Business Challenge

Despite its industry leadership in water treatment, the client faced a significant challenge: proactively managing water contamination, particularly in forecasting algae blooms and other pollutants. The goal was to build a machine learning solution capable of predicting contamination growth weeks in advance, allowing clients to act preemptively. This proactive approach aimed to achieve two main objectives:

• Cost Savings: Timely interventions reduce treatment costs for clients by addressing potential issues before they escalate.
• Enhanced Research Capabilities: By automating data processing and analysis, the client’s research team could uncover deeper insights into contamination patterns, monitor deviations, and tailor their services more effectively to the needs of each location.

Expected Outcome

The client envisioned a powerful, user-friendly dashboard that would allow research teams and customers to monitor data in real time and forecast contamination levels. Key expected outcomes included:

• Early Detection and Proactive Action: Enabling clients to respond to contamination threats well in advance.
• Operational Efficiency: Streamlining research team workflows and enhancing data-driven decision-making.
• Customizable Insights: Providing a dynamic interface where customers could filter and interact with data for specific sites, allowing for tailored action plans.

Our Approach

Leveraging our experience in time-series and AI solutions, we designed a solution architecture aligned with the client’s goals and technical needs. Our phased approach, which you can read here, ensured close collaboration and iterative improvements, starting from the discovery phase through to deployment.

Discovery Phase

The discovery phase served as the foundation of our solution design. Through a series of in-depth sessions with stakeholders—including management, customer-facing teams, and end-users—we identified core project goals and potential challenges. Our agenda for this phase included

• Stakeholder Identification and Requirement Gathering: Aligning with client expectations and establishing a roadmap for success.
• Resource Planning and Domain Knowledge Transfer: Understanding the technical landscape, identifying resource needs, and setting up protocols for knowledge sharing.
• Risk Mitigation and Contingency Planning: Addressing known risks proactively with mitigation strategies to ensure smooth project progress.

By working closely with both technical and non-technical stakeholders, we ensured all parties were aligned on the project’s vision and established a clear roadmap for implementation.

Data

Our data preparation process was a critical part of the project’s success. With a vast influx of sensor data coming from multiple sources, data quality was a top priority. We initiated a comprehensive data engineering and analysis phase to address the following:

• Site Selection and Sample Optimization: Recognizing the criticality of high-quality data for the success of any machine learning project, we carefully selected 5 candidate sites with at least 1 year of historical data available. This allowed us to fine-tune our machine learning models on high-quality, relevant datasets.
• Data Quality and Cleansing: While the real-world data presented challenges like anomalies, missing values, skewness, and data gaps, our experienced data engineering team was able to mitigate these issues through thorough investigation and correction techniques.
• Feature Engineering: A collaborative exchange with subject matter expertise between our team and client’s domain experts allowed us to verify certain hypotheses, identify key variables, uncover new influential factors, and engineer new meaningful features for the machine learning models.

This data-centric approach enabled us to design robust models that capture essential patterns and correlations within the time-series data.

AI Solution Design

With the data foundation established, we progressed to designing and implementing the machine learning solution. Our approach emphasized an iterative model-building and validation cycle:

1. Algorithm Selection and Evaluation: We adopted a simple-to-complex approach for model selection, starting with statistical methods and progressively testing more advanced techniques like decision trees, random forests, and XGBoost. The choice of algorithms was primarily influenced by the time-series nature of the data, emerging patterns, and volatility.
2. Experimentation and Model Fine-Tuning: We conducted multiple experimental runs in an MLOps environment, allowing for continuous monitoring and optimization of each model iteration.
3. User Journey and UI Design: To ensure transparency and active stakeholder involvement, our team designed an intuitive interface for clients to select locations, specify forecast parameters, and visualize predictions in real-time. That enabled all parties to track the progress, contribute to the model development lifecycle, minimized cognitive load and provided users with actionable insights.

To fast-track development, we utilized the Streamlit library for rapid prototyping, ensuring a responsive and efficient user experience. This agile approach allowed us to deliver quick wins, enabling early feedback from stakeholders and iterative improvements.

Development and Deployment

Our agile development process comprised several controlled experiments and validation phases to ensure model robustness and performance:

1. Iterative Model Experiments: Starting with a basic implementation, we gradually increased the complexity of model designs, fine-tuning hyperparameters and refining the input structure. By the final iterations, we achieved a model capable of accurately forecasting algae blooming factors.
2. Model Evaluation and Selection: Through continuous SME feedback, we optimized the solution, incorporating domain-specific insights that enhanced prediction accuracy. This collaborative approach ensured that our model met all specified requirements.
3. Web Interface and API Integration: To facilitate deployment, we created a REST API that consolidated the data pipeline and ML models into a single service, enabling seamless integration with the client’s existing systems. We also built a demonstration web UI, hosted on AWS, where the client’s management team could input parameters and view forecast analytics.

Key Outcomes

• Automated water contamination prediction system that can forecast algae blooms and other issues weeks in advance
• Significant cost savings for the end customers by optimizing the timing and frequency of water treatment interventions
• Enhanced productivity and deeper data insights for client’s research teams, enabling them to customize their water treatment solutions more effectively
• Streamlined data processing and model deployment pipeline that can be easily maintained and scaled

Testimonial

Why Choose TotemXLabs

At TotemXLabs, we’re not just code-slingers—we’re transformation architects. With a blend of high-octane data wizardry and precision-engineered AI, we don’t just meet project milestones; we set the bar higher. Our approach combines agile finesse with robust MLOps practices, ensuring that every solution isn’t just functional but a finely-tuned engine ready to tackle real-world challenges.

Think of us as your tech-savvy co-pilot, navigating the complexities of AI deployment with a laser focus on ROI. From feature engineering to model optimization, we break down the jargon and build up the results, turning your data into a strategic asset and every line of code into a competitive edge.

Still skeptical? Imagine a seamless CI/CD pipeline, user-friendly UX, and powerful predictive models—all delivered at the speed of innovation. With TotemXLabs, you’re not just getting a solution; you’re gaining a partner ready to propel you into the future of intelligent technology. Let’s shift the paradigm together.