Estimation of green and blue water evapotranspiration using machine learning algorithms with limited meteorological data: A case study in Amu Darya River Basin, Central Asia

Azzam, Abdullah; Zhang, Wanchang; Akhtar, Fazlullah; Shaheen, Zubair; Elbeltagi, Ahmed

doi:10.1016/j.compag.2022.107403

Cited by 21 publications

(5 citation statements)

References 66 publications

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“…11). Climate warming undoubtedly increased the rate of soil moisture evaporation and vegetation transpiration (Azzam et al, 2022). However, due to the general weakening of solar radiation and wind speed, the measured water surface evaporation at most meteorological and hydrological stations in China was decreasing, which also inhibited vegetation transpiration (Wang et al, 2018).…”

Section: The Spatial-temporal Variations Of the Gw Flow And Storage I...mentioning

confidence: 99%

“…Both the IPCC (Intergovernmental Panel on Climate Change) and the FAO (Food and Agriculture Organization of the United Nations) have ranked agriculture as one of the most vulnerable sectors to climate change, especially for developing countries (Veettil et al, 2022). The increase of temperature with the increase of CO 2 and other greenhouse gas emissions as well as the frequent occurrence of extreme climate events under climate change pose a direct impact on the growth of crops, which will reduce the amount of agriculture water resources available and intensify the contradiction between crop water supply and demand (Azzam et al, 2022). Additionally, the transformation of LULC types, the adjustment of planting area and structure will also have a significant impact on agricultural water supply and water demand (Sivakumar, 2011;Mehrotra et al, 2013;Li et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

Chi

Zhang

Wang

et al. 2023

Journal of Hydrology: Regional Studies

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Section: The Spatial-temporal Variations Of the Gw Flow And Storage I...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

Chi

Zhang

Wang

et al. 2023

Journal of Hydrology: Regional Studies

Self Cite

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“…Moreover, they cannot express complex relations between the input and the output. Many investigators have used artificial neural network (ANN) to model , such as Bruton, McClendon & Hoogenboom (2000) , Kumar et al (2002) , Xu et al (2006) , Chattopadhyay, Jain & Chattopadhyay (2009) , Yassin, Alazba & Mattar (2016) , Antonopoulos & Antonopoulos (2017) , Üneş et al (2018) , Gocić & Arab Amiri (2021) , Kaya et al (2021) , Pinos (2022) , Azzam et al (2022) , Dadrasajirlou et al (2022) , Heramb et al (2023) , and Patle et al (2023) . Researchers reported that, like a decision tree, the ANN-based models are also prone to overfitting and require large datasets for their training ( Pinos, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…Researchers reported that, like a decision tree, the ANN-based models are also prone to overfitting and require large datasets for their training ( Pinos, 2022 ). Furthermore, there is no standard method to determine the structure of a neural network for modeling a problem ( Azzam et al, 2022 ). Instead of using a standalone application, several hybrid systems endeavoured to provide better solutions.…”

Section: Introductionmentioning

confidence: 99%

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Banerjee,

Sarkar,

Sarkar

et al. 2024

PeerJ

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Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

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“…Hu et al (2021b) used a deep neural network, RF, and symbolic regression to estimate evapotranspiration using meteorological and plant data. These attempts have proven that machine learning methods are effective tools for accurate evapotranspiration estimation [27][28][29]. In particular, the tree-based ensemble model exhibits high superiority in ET a estimations for multiple ecosystems in different climatic regions [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Daily Actual Evapotranspiration of Tea Plantations Using Ensemble Machine Learning Algorithms and Six Available Scenarios of Meteorological Data

Geng,

Li,

Luan

et al. 2023

Applied Sciences

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The tea plant (Camellia sinensis), as a major, global cash crop providing beverages, is facing major challenges from droughts and water shortages due to climate change. The accurate estimation of the actual evapotranspiration (ETa) of tea plants is essential for improving the water management and crop health of tea plantations. However, an accurate quantification of tea plantations’ ETa is lacking due to the complex and non-linear process that is difficult to measure and estimate accurately. Ensemble learning (EL) is a promising potential algorithm for accurate evapotranspiration prediction, which solves this complexity through the new field of machine learning. In this study, we investigated the potential of three EL algorithms—random forest (RF), bagging, and adaptive boosting (Ad)—for predicting the daily ETa of tea plants, which were then compared with the commonly used k-nearest neighbor (KNN), support vector machine (SVM), and multilayer perceptron (MLP) algorithms, and the experimental model. We used 36 estimation models with six scenarios from available meteorological and evapotranspiration data collected from tea plantations over a period of 12 years (2010–2021). The results show that the combination of Rn (net radiation), Tmean (mean air temperature), and RH (relative humidity) achieved reasonable precision in assessing the daily ETa of tea plantations in the absence of climatic datasets. Compared with other advanced models, the RF model demonstrated superior performance (root mean square error (RMSE): 0.41–0.56 mm day−1, mean absolute error (MAE): 0.32–0.42 mm day−1, R2: 0.84–0.91) in predicting the daily ETa of tea plantations, except in Scenario 6, followed by the bagging, SVM, KNN, Ad, and MLP algorithms. In addition, the RF and bagging models exhibited the highest steadiness with low RMSE values increasing (−15.3~+18.5%) in the validation phase over the testing phase. Considering the high prediction accuracy and stability of the studied models, the RF and bagging models can be recommended for estimating the daily ETa estimation of tea plantations. The importance analysis from the studied models demonstrated that the Rn and Tmean are the most critical influential variables that affect the observed and predicted daily ETa dynamics of tea plantations.

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Estimation of green and blue water evapotranspiration using machine learning algorithms with limited meteorological data: A case study in Amu Darya River Basin, Central Asia

Cited by 21 publications

References 66 publications

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration

Estimation of Daily Actual Evapotranspiration of Tea Plantations Using Ensemble Machine Learning Algorithms and Six Available Scenarios of Meteorological Data

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