A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Abstract

Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co-locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin-one of the United States' most nutrient polluted basinsusing the established Agro-IBIS and THMB models. Synthetic data enables rapid, unbiased and low-cost assessment of potential sensor placements to support management objectives, such as near-term forecasting. Using the synthetic data, we established baseline 1-day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48-3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co-benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near-term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

Data science applications in hydrology

“…Perhaps the most immediate and powerful use of data science methods in hydrology, and specifically of machine and deep learning methods, is for prediction and forecasting. A number of papers in this issue provide interesting examples of the use of gradient‐boosted decision trees and long short‐term memory networks to forecast sediment concentration and transport (Kim et al, 2022; Lund et al, 2022); the use of a range of machine learning methods for evapotranspiration estimation (Mangalath Ravindran et al, 2022) using AutoML, a framework designed to bring machine learning methods to non‐experts; or the use of support vector machines to forecast nitrate concentration in rivers and inform the development of water‐quality sensor networks (Balson & Ward, 2022). These papers show the growing interest in Machine and Deep Learning methods for prediction and forecasting in our field, among other things because these methods typically outperform classic hydrologic models.…”