We write tons of code in our daily work, and we make the most reliable solutions available on GitHub. Aside from Simulation software, we develop analytics solutions that extract information from data with the ultimate goal of aiding the management of water resources systems. Our analytics can be organized into Descriptive, Predictive, and Prescriptive analytics.

Descriptive analytics rely on statistical learning to provide key insights about past data. Input variable selection algorithms, for example, are typically used to understand which variables—among a large set—contain the information necessary for describing a physical process of interest. Descriptive analytics can be coupled with Diagnostic and Predictive analytics, which provide estimates about the current and future state of a system.

Prescriptive analytics go one step further by recommending which courses of actions are most likely to improve the performance of a water system. These analytics find their roots in Operations Research, Optimal Control Theory, and Reinforcement Learning.

Analytics solutions are not mutually exclusive; they rather complement each other and contribute—in different forms—to improved decision-making.