Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands

Wetlands are extraordinary ecosystems and important climate regulators that also contribute to reduce natural disaster risk. Unfortunately, wetlands are declining much faster than forests. The safeguarding of the wetlands also needs knowledge of the dynamics that control the water balance of these environments. Therefore, an accurate estimation of evapotranspiration in wetlands is an essential task. When adequate experimental data are available, some algorithms deriving from Artificial Intelligence research represent a promising alternative to the most common estimation techniques. In this study, starting from daily measurements of climatic variables such as net solar radiation, depth to water, wind speed, mean relative humidity, maximum temperature, minimum temperature, and mean temperature, using the Random Forest, Additive Regression of Decision Stump, Multilayer Perceptron and k-Nearest Neighbors algorithms, 24 estimation models, different in input variables, have been developed and compared. The data have been provided by USGS. They have been obtained from a measuring site in wetlands of Indian River County, Florida using the eddy-covariance technique. The accuracy of these models based on AI algorithms remains good even if the number of input variables is reduced from 7 to 3. Net solar radiation, mean temperature and mean relative humidity or wind speed measurements allow obtaining a sufficiently accurate estimation model. Random Forest and k-Nearest Neighbors provide slightly better performance than Additive Regression of Decision Stump and Multilayer Perceptron. The analyzed models show in most cases the lowest accuracy in the range 2-4 mm/day, while the highest accuracy is obtained in the ranges 0-2 mm/day and 6-8 mm/day, with the exception of the models based on the Additive Regression, which show similar levels of accuracy in the different considered sub-intervals.