Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework
| Autor: | Debaditya Chakraborty, James Winterle, Hakan Başağaoğlu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Boosting (machine learning)
lcsh:Hydraulic engineering 010504 meteorology & atmospheric sciences Geography Planning and Development 0207 environmental engineering MathematicsofComputing_GENERAL evapotranspiration 02 engineering and technology Aquatic Science Machine learning computer.software_genre 01 natural sciences Biochemistry probabilistic model lcsh:Water supply for domestic and industrial purposes shapley analysis lcsh:TC1-978 Evapotranspiration 020701 environmental engineering GeneralLiterature_REFERENCE(e.g. dictionaries encyclopedias glossaries) Pan evaporation 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 business.industry Probabilistic logic Prediction interval Statistical model Trustworthiness TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES machine learning Environmental science Artificial intelligence business computer Test data |
| Zdroj: | Water, Vol 13, Iss 557, p 557 (2021) Water Volume 13 Issue 4 |
| ISSN: | 2073-4441 |
| Popis: | Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily ETo, Esw, & ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa test data at three watersheds were within the models’ 95% prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict ETa while offsetting high capital & operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|