Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber–Physical Systems

Wang, Tianteng; Wang, Xuping; Jiang, Yi-Ping; Sun, Zilai; Liang, Yuhu; Hu, Xiangpei; Li, Hao; Shi, Yan; Xu, Jingjun; Ruan, Junhu

doi:10.1109/tcyb.2022.3164542

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…Also, LSTM-ANN recommends a hybrid approach for ET o estimation. The estimation of ET o has been predicted by Wang et al [59] through the utilization of a combination of time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO). Subsequently, GBDT is deployed to anticipate evapotranspiration, while BO determines the optimum hyperparameter values from the pared-down granules.…”

Section: Study Discussionmentioning

confidence: 99%

“…The effectiveness of various types of ML algorithms, including tree-based, neural network-based, multifunctionbased algorithms, and a combination of ML and physical models, have been investigated in predicting hydrological variables (ET o , river discharge, precipitation, monitoring droughts) and their related factors [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74]. Wang et al [59] analyzed temperature data from several different climatic stations located in Pakistan [60]. The TB model produced outperforming results (R 2 and NSE = 1.00, MAE and RMSE = 0.26 and 0.37) when an input combination based only on temperatures (T max and T min ) was used.…”

Section: Study Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Raza,

Fahmeed,

Syed

et al. 2023

Water

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The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.

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Section: Study Discussionmentioning

confidence: 99%

Section: Study Discussionmentioning

confidence: 99%

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Raza,

Fahmeed,

Syed

et al. 2023

Water

View full text Add to dashboard Cite

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“…The system is supported by WiFi-based IoT that can send data directly to an IoT-based web server and serves as a distributed monitoring system [79]. A cyber-physical system for crop evapotranspiration estimation is proposed [80]. A gradient-boosting decision tree along with a fuzzy granulation method is used on IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China for cherry tree evapotranspiration estimation and the proposed system achieved promising accuracy [81].…”

Section: Agriculturementioning

confidence: 99%

AI-based fog and edge computing: A systematic review, taxonomy and future directions

et al. 2023

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“…Several studies have explored the integration of NNs into stochastic DEA models. For example, Lu et al (2009) proposed a method that uses a feedforward NN to estimate the conditional mean and variance of the input and output data of the DMUs, and then uses a stochastic DEA model to calculate the efficiency scores (Boubaker et al, 2023) (Wang et al, 2022) (Kainthura & Sharma, 2022). Zhang et al (2014) proposed a similar method that uses a radial basis function NN to estimate the conditional distribution of the input and output data of the DMUs (Pendharkar, 2023).…”

Section: Introductionmentioning

confidence: 99%

Integrating the neural network into the stochastic DEA model

Sihotang,

Lavemaau,

Riandari

et al. 2023

Idea

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The novelty of integrating neural networks (NNs) into the stochastic DEA model lies in the ability to address some of the limitations of traditional DEA models and improve the accuracy and efficiency of efficiency measurement and prediction. By integrating NNs, the stochastic DEA model can capture the complex and non-linear relationships between the input and output variables of the decision-making units (DMUs) and handle uncertainty in the input and output data. This is achieved by using the NN to estimate the distribution of the input and output data and then using the stochastic DEA model to calculate the efficiency scores based on these estimated distributions. Furthermore, the integration of NNs into the stochastic DEA model allows for the development of hybrid models that combine the strengths of both techniques. For example, some researchers have proposed using genetic algorithms or other optimization techniques to optimize the input and output weights of the stochastic DEA model, which are then used to calculate the efficiency scores based on the estimated distributions from the NN. This results in a more accurate and efficient efficiency measurement and prediction model. Another novelty of integrating NNs into the stochastic DEA model is the potential for enhancing the interpretability of the model. While NNs are often considered as black-box models, several methods have been proposed to enhance the interpretability of NN-based stochastic DEA models. These methods include using feature importance analysis or visualization techniques to identify the most important input and output variables that contribute to the efficiency scores. Overall, the integration of NNs into the stochastic DEA model represents a novel approach to addressing the limitations of traditional DEA models and improving the accuracy and efficiency of efficiency measurement and prediction under uncertainty. The development of hybrid models and methods to enhance interpretability further add to the novelty and potential impact of this research.

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Hybrid Machine Learning Approach for Evapotranspiration Estimation of Fruit Tree in Agricultural Cyber–Physical Systems

Cited by 7 publications

References 29 publications

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

Performance Evaluation of Five Machine Learning Algorithms for Estimating Reference Evapotranspiration in an Arid Climate

AI-based fog and edge computing: A systematic review, taxonomy and future directions

Integrating the neural network into the stochastic DEA model

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