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.

Precision Irrigation Strategies for Sustainable Water Budgeting of Potato Crop in Prince Edward Island

“…Comparison of rainfall with reference evapotranspiration for the period 2011-2017. Reproduced with permission from Afzaal et al, Applied Sciences; MDPI, 2020[33].…”

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

“…Afzaal et al [76] used LSTM and BiLSTM for estimating ETo using air temperature and relative humidity as the only variables. Both DL models showed high accuracy compared to the actual ETo, with less difference in performance between the two models.…”

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

“…As a special kind of recurrent neural network structure, the LSTM model is one of the most popular neural network models in nonlinear time series analyses because it can store and relate previous information in a sequence, enabling it to predict time series in near real-time. LSTM has been widely used in hydrological simulation and prediction, extreme flood monitoring, and precise water resource management [66,67].…”