Climate change is changing landscapes and technology is racing to keep up. Government agencies are struggling to fund research and monitoring projects throughout the world, and this has allowed the opportunity for citizen scientists to get involved in monitoring the impacts of climate change. This research outlines a relatively low-cost prototype, made up of readily available technology, like household or commercial sensors and open-source software. This prototype can be adapted, for different sensors and monitoring requirements, to carry out monitoring along river stretches in an intelligent way. Sensor fusion will be used to maximise the information gained from the sensors. PCA analysis shows that testing parameters support the inclusion of four dimensions at 87% variance, but six will capture 97%. Testing datasets are small and further tests will clarify this in the future. Key to this prototype will be an embedded Random Forest model, trained on large water quality datasets, with an F-score of 0.85 and capable of dictating navigation parameters depending on the data received from the on-board water quality sensors in real-time. The target of collecting data from an array of sensors and using the data to control an autonomous vehicle has been tentatively achieved and future directions could be for greater sensor fusion and developing the prototype for waypoint following.