Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

Abstract

Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011−2015) and testing (2016−2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016−2017) root mean square errors were recorded in range of 0.38−0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.

Keywords

• recurrent neural networks
• deep learning
• irrigation scheduling
• penman–monteith
• physical hydrology components
• water cycle budgeting