Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data

Sevim Seda Yamaç and Mladen Todorovic

Agricultural Water Management, 2020, vol. 228, issue C

Abstract: Crop evapotranspiration (ETc) is a complex and non-linear process difficult to measure and estimate accurately. This complexity can be solved applying the machine learning techniques with different meteorological input variables. This study investigated the performance of k-Nearest Neighbour (kNN), Artificial Neural Networks (ANN) and Adaptive Boosting (AdaBoost) models to predict daily potato ETc using four scenarios of available meteorological data as: air temperature (scenario 1), air temperature and solar radiation (scenario 2), air temperature, solar radiation and wind speed (scenario 3), and air temperature, solar radiation, wind speed and relative humidity (scenarios 4). The analysis was based on the results of experimental trials carried out in Southern Italy in 2009 and 2010 and focussed on the potato crop cultivation under optimal water supply. The results of ETc estimation with different machine learning techniques were compared with ETc obtained from the soil water balance model, based on the FAO Penman Monteith approach, and gravimetric measurements of soil water content in the crop root zone. The best performances were observed with the kNN model with R2 of 0.813, 0.968 and 0.965, slope of regression 0.947, 0.980 and 0.991, modelling efficiency (EF) of 0.848, 0.970 and 0.972, root mean square error (RMSE) of 0.790, 0.351 and 0.355 mm day−1, mean absolute error (MAE) of 0.563, 0.263 and 0.274 mm day−1 and mean squared error (MSE) of 0.623, 0.123 and 0.126 mm day−1 for scenarios 1, 2 and 3, respectively. When all meteorological variables were available (scenario 4), the ANN model produced slightly better statistical indicators. Therefore, the kNN model could be recommended for the estimation of ETc when limited meteorological data are available. Otherwise, the ANN model should be applied.

Keywords: Artificial neural networks; k-Nearest neighbour; Adaptive boosting; Mediterranean region; Penman-Monteith equation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378377419308352
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:228:y:2020:i:c:s0378377419308352

DOI: 10.1016/j.agwat.2019.105875

Access Statistics for this article

Agricultural Water Management is currently edited by B.E. Clothier, W. Dierickx, J. Oster and D. Wichelns

More articles in Agricultural Water Management from Elsevier
Bibliographic data for series maintained by Catherine Liu ().