Random forests in water resources
- 1Hellenic Air Force General Staff, Hellenic Air Force, Athens, Greece (montchrister@gmail.com)
- 2School of Civil Engineering, National Technical University of Athens, Zografou, Greece (hristos@itia.ntua.gr)
- 3School of Engineering, University of Patras, Patras, Greece (geopap@upatras.gr)
- 4Department of Engineering, Roma Tre University, Rome, Italy (papacharalampous.georgia@gmail.com)
- 5School of Engineering, University of Patras, Patras, Greece (andlag@alum.mit.edu)
Random forests is a supervised machine learning algorithm which has witnessed recently an exponential increase in its implementation in water resources. However, the existing implementations have been restricted in applications of Breiman’s (2001) original algorithm to regression and classification models, while numerous developments could be also useful for solving diverse practical problems. Here we popularize random forests for the practicing hydrologist and present alternative random forests based algorithms and related concepts and techniques, which are underappreciated in hydrology. We review random forests applications in water resources and provide guidelines for the full exploitation of the potential of the algorithm and its variants. Relevant implementations of random forests related software in the R programming language are also presented.
How to cite: Tyralis, H., Papacharalampous, G., and Langousis, A.: Random forests in water resources, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2105, https://doi.org/10.5194/egusphere-egu21-2105, 2021.
