Improvement of sap flow estimation by including phenological index and time-lag effect in back-propagation neural network models

Tu, Jie; Wei, Xiaohua; Huang, Bingbing; Fan, Hongsong; Jian, Minfei; Li, Wei

doi:10.1016/j.agrformet.2019.06.007

Cited by 22 publications

(31 citation statements)

References 57 publications

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“…Because the soil water content increases sharply after precipitation, and the sap flow increases slowly due to the physiological control of the plant, there may be a nonlinear relationship between the sap flow and the soil water content, which is not in line with the basic statistical method assumptions. The reason may also be that soil moisture is not a main limiting factor because the stands do not suffer from water stress all the time (other studies have shown that soil water is not an important driving variable for transpiration, which may be due to humid conditions (Tu et al, 2019)) according to a study that divided REW into several stages (Fu et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…However, recent studies have shown that there is a nonlinear and complex interaction between transpiration and control factors (Liu et al, 2019; Oogathoo et al, 2020), which is different from the basic setting of traditional statistical methods. Because the traditional method assumes that the correlation relationship obeys a smooth and continuous linear polynomial curve, the field test data are not completely consistent with the statistical analysis results (Tu et al, 2019). Moreover, the impact of variables on sap flow varies with climate region, soil characteristics, tree species and tree age.…”

Section: Introductionmentioning

confidence: 99%

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The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back‐propagation neural network model

Chen

et al. 2022

Hydrological Processes

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Tree transpiration is an important water movement process in forest ecosystems, and it plays a decisive role in the coupling feedback of hydrological and ecological processes. Therefore, identifying the impact of different factors on sap flow can promote efficient water management and improve assessment of the climate change impacts. However, the interaction between sap flow and control factors is not clear, and there is no accurate model to predict sap flow change of Picea crassifolia Kom. This study explored the correlation between sap flow and environmental factors and compared the performances of the back‐propagation neural network (BPANN) and multiple regression (MLR) models, and the importance of the time‐lag effect and different soil moisture conditions in the response mechanism of sap flow was also explained. A higher fitting performance was found in the BPANN model (R2 > 0.90) based on the relationships between air temperature (Ta), air relative humidity (RH), solar radiation (Rn) and vapour pressure deficit (VPD) and sap flux density (Q) than that MLR (R2 = 0.8915). The correlation was improved due to the consideration of time‐lag effect. Other variables, such as maximum temperature (Tmax), wind speed (WS) and precipitation (P), explained smaller portions of the variance in sap flow, while minimum temperature (Tmin) and leaf area index (LAI) had almost no effect. Moreover, the R2 of the water stress condition (REW <0.38) was lower than that of the non‐water stress condition (REW ≥0.38), and even lower than the R2 of the whole experimental period. Therefore, the sap flow prediction model based on BPANN could more reasonably explain the nonlinear relationship between transpiration and control factors, which provided a basis for the estimation of plant water‐use and the construction and management of ecological vegetation in alpine arid and semi‐arid areas, especially in response to the continuous enhancement of aridification and climate warming.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back‐propagation neural network model

Chen

et al. 2022

Hydrological Processes

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“…Based on the forward-propagating error information, the BPNN uses the gradient descent method to modify the network's multilayer connection weights and thresholds layer by layer from back to front, until it reaches the termination condition. Its main applications are in information analysis, image processing, and data optimization [27,28] .…”

Section: Soil Moisture Regulation Modelmentioning

confidence: 99%

Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

Wan

Chen

et al. 2021

International Journal of Agricultural and Biological Engineering

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Soil moisture is a major environmental factor that influences tomato growth and development. Suitable soil moisture not only increases tomato production but also saves irrigation water. In this study, an irrigation decision model was developed, which called soil moisture regulation model, for optimizing growth of tomato seedlings while saving water. The data used for modeling were collected from a multi-gradient nested experiment, in which temperature, photosynthetic photon flux density (PPFD), carbon dioxide (CO 2 ) concentration and soil moisture were variables and the corresponding photosynthetic rate was measured. Subsequently, a prediction model of tomato photosynthetic rate was constructed using support vector regression (SVR) algorithm. With photosynthetic rate prediction model as fitness function, genetic algorithm (GA) was used to find the optimal soil moisture under each combination of the above environmental factors. Finally, back propagation neural network (BPNN) algorithm was used to establish a decision model of tomato irrigation, which could provide the optimal soil moisture under current environment. For the soil moisture regulation model constructed here, the coefficient of determination was 0.9738, the mean square error of the test set was 1.51×10 -5 , the slope of the verified straight line was 0.9752, and the intercept was 0.00916. This model demonstrated high precision, which thereby provides a theoretical basis for accurate irrigation control in the greenhouse facility environment.

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“…where X max , X min are the maximum and minimum of all values applied to the variables and X, X norm are the measured values of all the variables before and after treatment, respectively [12,21,36].…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

“…Though the mathematical model derived from Fick's second law [6][7][8][9][10] and the pseudo-second-order kinetic model [11] have been established to predict the dyeing effect in SC-CO 2 , the lack of the parameters such as diffusion coefficient limits their applications. Artificial neural network (ANN) with superior ability to learn and classify data which come from studies on the function of the brain and nerve systems as well as the mechanism of learning and responding [12,13]. Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are two commonly paradigms [14,15].…”

Section: Introductionmentioning

confidence: 99%

Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Zhang

Sun

et al. 2020

Processes

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With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

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Improvement of sap flow estimation by including phenological index and time-lag effect in back-propagation neural network models

Cited by 22 publications

References 57 publications

The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back‐propagation neural network model

The accuracy improvement of sap flow prediction in Picea crassifolia Kom. based on the back‐propagation neural network model

Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

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