scholarly journals Moisture Prediction from Simple Micrometeorological Data

1999 ◽  
Vol 89 (8) ◽  
pp. 668-672 ◽  
Author(s):  
Y. Chtioui ◽  
L. J. Francl ◽  
S. Panigrahi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Flag Leaf ◽  
Linear Method ◽  
Regression Tree ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Tree Model ◽  
Regression Methods ◽  
Plant Disease Management

Four linear regression methods and a generalized regression neural network (GRNN) were evaluated for estimation of moisture occurrence and duration at the flag leaf level of wheat. Moisture on a flat-plate resistance sensor was predicted by time, temperature, relative humidity, wind speed, solar radiation, and precipitation provided by an automated weather station. Dew onset was estimated by a classification regression tree model. The models were developed using micrometeorological data measured from 1993 to 1995 and tested on data from 1996 and 1997. The GRNN outperformed the linear regression methods in predicting moisture occurrence with and without dew estimation as well as in predicting duration of moisture periods. Average absolute error for prediction of moisture occurrence by GRNN was at least 31% smaller than that obtained by the linear regression methods. Moreover, the GRNN correctly predicted 92.7% of the moisture duration periods critical to disease development in the test data, while the best linear method correctly predicted only 86.6% for the same data. Temporal error distribution in prediction of moisture periods was more highly concentrated around the correct value for the GRNN than linear regression methods. Neural network technology is a promising tool for reasonably precise and accurate moisture monitoring in plant disease management.

Estimation of maize evapotranspiration using extreme learning machine and generalized regression neural network on the China Loess Plateau

2016 ◽  
Vol 48 (4) ◽  
pp. 1156-1168 ◽  
Author(s):  
Yu Feng ◽  
Daozhi Gong ◽  
Xurong Mei ◽  
Ningbo Cui
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Loess Plateau ◽  
Meteorological Data ◽  
Crop Coefficient ◽  
Absolute Error ◽  
Generalized Regression Neural Network ◽  
Area Index ◽  
Generalized Regression ◽  
Learning Machine

Accurately estimating crop evapotranspiration (ET) is essential for agricultural water management in arid and semiarid croplands. This study developed extreme learning machine (ELM) and generalized regression neural network (GRNN) models for maize ET estimation on the China Loess Plateau. Maize ET, meteorological variables, leaf area index (LAI), and plant height (hc) were continuously measured during maize growing seasons of 2011–2013. The meteorological data and crop data including LAI and hc from 2011 to 2012 were used to train the ELM and GRNN using two different input combinations. The performances of ELM and GRNN were compared with the modified dual crop coefficient (Kc) approach in 2013. Results indicated that ELM1 and GRNN1 using meteorological and crop data as inputs estimated maize ET accurately, with root mean square error (RMSE) of 0.221 mm/d, mean absolute error (MAE) of 0.203 mm/d, and NS of 0.981 for ELM1, RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981 for GRNN1, respectively, which confirmed better performances than the modified dual Kc model. Performances of ELM2 and GRNN2 using only meteorological data as input were poorer than those of ELM1, GRNN1, and modified dual Kc approach, but its estimation of maize ET was acceptable when only meteorological data were available.

scholarly journals Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

2017 ◽  
Vol 62 (4) ◽  
pp. 825-841 ◽  
Author(s):  
J. Jakubowski ◽  
J.B. Stypulkowski ◽  
F.G. Bernardeau
Keyword(s):  
Linear Regression ◽  
Evaluation System ◽  
Regression Tree ◽  
Soft Rock ◽  
Multivariate Linear Regression ◽  
Pressure Balance ◽  
Tbm Performance ◽  
Regression Methods ◽  
Geotechnical Characteristics ◽  
Boring Machines

Abstract The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Modeling and parameter optimization of the papermaking processes by using regression tree model and full factorial design

TAPPI Journal ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 123-137
Author(s):  
JOSÉ L. RODRIGUEZ-ALVAREZ, ◽  
ROGELIO LOPEZ-HERRERA ◽  
IVÁN E. VILLALON-TURRUBIATES ◽  
GERARDO GRIJALVA-AVILA ◽  
JORGE L. GARCÍA ALCARAZ
Keyword(s):  
Linear Regression ◽  
Factorial Design ◽  
Regression Tree ◽  
Full Factorial Design ◽  
Operating Conditions ◽  
Tree Model ◽  
Predictive Capacity ◽  
Output Pressure ◽  
Full Factorial ◽  
Regression Tree Model

One of the major challenges in the pulp and paper industry is taking advantage of the large amount of data generated through its processes in order to develop models for optimization purposes, mainly in the paper-making, where the current practice for solving optimization problems is the error-proofing method. First, the multi-ple linear regression technique is applied to find the variables that affect the output pressure controlling the gap of the paper sheet between the rod sizer and spooner sections, which is the main cause of paper breaks. As a measure to determine the predictive capacity of the adjusted model, the coefficient of determination (R2) and s values for the output pressure were considered, while the variance inflation factor was used to identify and elimi-nate the collinearity problem. Considering the same amount of data available by using machine learning, the regres-sion tree was the best model based on the root mean square error (RSME) and R2. To find the optimal operating con-ditions using the regression tree model as source of output pressure measurement, a full factorial design was developed. Using an alpha level of 5%, findings show that linear regression and the regression tree model found only four independent variables as significant; thus, the regression tree model demonstrated a clear advantage over the linear regression model alone by improving operating conditions and demonstrating less variability in output pressure. Furthermore, in the present work, it was demonstrated that the adjusted models with good predictive capacity can be used to design noninvasive experiments and obtain.

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