Leveraging IoT and Machine Learning for Improved Monitoring of Water Resources - A Case Study of the Upper Ewaso Nyiro River

This paper outlines the development of an Internet of Things (IoT) system, capable of water-level monitoring in rivers, with an aim of ensuring equitable distribution of water and sourcing of data to quantify unsustainable water usage, river catchment destruction and climate change. The paper also defines an application scenario in a specific hydrological region of the Ewaso Nyiro basin in Kenya, highlighting the characteristics of data collection and processing used. Fixed position node systems are described along with web-based data acquisition platform developments, integrated with IoT techniques to retrieve data. The developed architecture utilizes the LoRaWAN - LoRa protocol to send data packets from nodes deployed, to an IoT network server. From the server, data is transferred to a database, where it can be accessed and displayed through different customizable queries and graphical representations. Outlined characteristics are presented along with evidence of the deployment of different devices and of the IoT network infrastructure.