Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods

Chen, Zhijun; Zhu, Zhenchuang; Jiang, Hao; Sun, Shijun

doi:10.1016/j.jhydrol.2020.125286

Cited by 146 publications

(58 citation statements)

References 41 publications

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“…Both DL models showed high accuracy compared to the actual ETo, with less difference in performance between the two models. Chen et al [77] evaluate the performance of three DL methods of deep DNN, temporal convolution network (TCN) [78], and long short-term memory neural network (LSTM) for reference evapotranspiration estimation. The results showed that DL model outperformed other conventional models, and TCN and LSTM showed outstanding performance when temperature features were used.…”

Section: Evapotranspiration Estimationmentioning

confidence: 99%

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

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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Section: Evapotranspiration Estimationmentioning

confidence: 99%

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

2021

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“…A pure artificial neural network (ANN) was proven to have good performance in retrieving land surface fluxes, or in some cases, even better performance than that of hybrid models (Chen et al, 2020;Haughton et al, 2018;Zhao et al, 2019). In this study, we trained a multi-layer feedforward neural network model that consisted of an input layer, hidden layers, and an output layer to predict daily λE and H at the globally distributed weather stations.…”

Section: Artificial Neural Network Model Trainingmentioning

confidence: 99%

Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces

Wang¹,

Gentine²,

Yin³

et al. 2020

Preprint

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Abstract. Evapotranspiration (ET) accompanied by water and heat transport in the hydrological cycle is a key component in regulating surface aridity. Existing studies on changes in surface aridity have typically estimated ET using semi-empirical equations or parameterizations of land surface processes, which are based on the assumption that the parameters in the equation are stationary. However, plant physiological effects and its response to a changing environment are dynamically modifying ET, thereby challenging this assumption and limiting the estimation of long-term ET. In this study, the latent heat flux (ET in energy units) and sensible heat flux were retrieved for recent decades on a global scale using machine learning approach and driven by ground-based observations from flux towers and weather stations. The study resulted in several findings, namely that the evaporative fraction (EF) – the ratio of latent heat flux to available surface energy – exhibited a relatively decreasing trend on fractional land surfaces; In particular, the decrease in EF was accompanied by an increase in long-term runoff as assessed by precipitation (P) minus ET, accounting for 27.06 % of the global land areas. The signs were indicative of reduced surface conductance, which further emphasized that land-surface vegetation has major impacts on regulating the water and energy cycles, as well as aridity variability.

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“…In recent years, however, artificial intelligence-based methods such as the neural networks (Kisi et al 2015;Gavili et al 2018), the support vector machine (Tabari et al 2013), the extreme learning machine (Abdullah et al 2015), decision tree (Huang et al 2019;Raza et al 2020), and hybrid methods (Ehteram et al 2019;Shiri et al 2020;Tikhamarine et al 2019;Zhu et al 2020;Kim et al 2014;Sanikhani et al 2019;Mehdizadeh et al 2017) have had many applications in estimating ET Ref , but among them, multivariate linear regression method has been compared with other empirical equations and soft computing, validated by many researchers (Reis et al 2019;Kisi and Heddam 2019;Mattar and Alazba 2019;Tabari et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…According to their results, Turc empirical formula (radiation-based) is found better than other empirical equations and the highest correlation coefficient is calculated for ANFIS, and the minimum errors are calculated for radial basis function SVM. Also, Chen et al (2020) estimate daily ET Ref based on limited meteorological data using three deep learning methods, two classical machine learning methods, and seven empirical equations. Their results show that, when temperature-based features were available, the deep learning models performed markedly better than temperature-based empirical models, and when radiation-based or humiditybased features were available, all of the proposed deep and classical learning machine models outperformed radiationbased or humidity-based empirical equations beyond the study areas.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

Sharafi

Ghaleni

2021

Theor Appl Climatol

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The study aimed to evaluate the accuracy of empirical equations (Hargreaves-Samani; HS, Irmak; IR and Dalton; DT) and multivariate linear regression models (MLR1–6) for estimating reference evapotranspiration (ETRef) in different climates of Iran based on the Köppen method including arid desert (Bw), semiarid (Bs), humid with mild winters (C), and humid with severe winters (D). For this purpose, climatic data of 33 meteorological stations during 30 statistical years 1990–2019 were used with a monthly time step. Based on various meteorological data (minimum and maximum temperature, relative humidity, wind speed, solar radiation, extraterrestrial radiation, and vapor pressure deficit), in addition to 6 multivariate linear regression models and three empirical equations were used as MLR1, MLR2, and HS (temperature-based), MLR3 and IR (radiation-based), MLR4, MLR5 and DT (mass transfer-based), and MLR6 (combination-based) were also used to estimate the reference evapotranspiration. The results of these models were compared using the root mean square error (RMSE), mean absolute error (MAE), scatter index (SI), determination coefficient (R2), and Nash-Sutcliffe efficiency (NSE) statistical criteria with the evapotranspiration results of the FAO56 Penman-Monteith reference as target data. All MLR models gave better results than empirical equations. The results showed that the simplest regression model (MLR1) based on the minimum and maximum temperature data was more accurate than the empirical equations. The lowest and highest accuracy related to the MLR6 model and HS empirical equation with RMSE was 10.8–15.1 mm month−1 and 22–28.3 mm month−1, respectively. Also, among all the evaluated equations, radiation-based models such as IR in Bw and Bs climates with MAE = 8.01–11.2 mm month−1 had higher accuracy than C and D climates with MAE = 13.44–14.48 mm month−1. In general, the results showed that the ability of regression models was excellent in all climates from Bw to D based on SI < 0.2.

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Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods

Cited by 146 publications

References 41 publications

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

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

Long-term relative decline in evapotranspiration with increasing runoff on fractional land surfaces

Evaluation of multivariate linear regression for reference evapotranspiration modeling in different climates of Iran

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