A framework for smart assessment of river health using machine learning

There is a current concern for the health of river ecosystems due to their vulnerability and increasing deterioration from human pressures, as well as the interest in achieving freshwater environmental sustainability in the well-known climate change challenges.

Analysis of international monitoring frameworks of river health have highlighted the need to increase data availability and frequency as well as reduce data uncertainty. With this, new aggregation, standardization, and classification methods are required as the development of technologies have grown and reached citizens at different social and cultural levels, their participation have increased in the recent years, showing important time-cost advantages. However, still there are no clear protocols to implement as assessment using mobile phone tools and platforms. 

This study aims to develop a dynamic framework for smart river health ecosystem monitoring, employing citizen science and remote sensing. This concept uses hydro-morphological and biological river indicators, combined with machine learning algorithms to analyze spatiotemporal data. 

The smart framework for assessment presented here aims to be provide to 1) Characterized  natural and non-natural changes of river ecosystem health; 2) Improve river monitoring methods linking local observation and remote sensing data; 3) Develop databases and data visualization of river condition components; 4) Enable citizens to become a large sensor network to contribute to river health monitoring; and 5) Determine and georeferenced the causes of  river health changes to support nature-based solutions for river ecosystem management.