DEVELOPMENT AND VALIDATION OF A NEURAL NETWORK MODEL FOR SOIL WATER CONTENT PREDICTION WITH COMPARISON TO REGRESSION TECHNIQUES

1999 ◽  
Vol 42 (3) ◽  
pp. 691-700 ◽  
Author(s):  
C. T. Altendorf ◽  
R. L. Elliott ◽  
E. W. Stevens ◽  
M. L. Stone
Keyword(s):  
Neural Network ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Network Model ◽  
Neural Network Model ◽  
Regression Techniques ◽  
Development And Validation

scholarly journals Development and Validation of a New Calibration Model for Diviner 2000® Probe Based on Soil Physical Attributes

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3414
Author(s):  
Giuseppe Provenzano ◽  
Giovanni Rallo ◽  
Ceres Duarte Guedes Cabral de Almeida ◽  
Brivaldo Gomes de Almeida
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Efficiency Index ◽  
Calibration Model ◽  
Volumetric Soil Water Content ◽  
Physical Attributes ◽  
Water Volumes ◽  
Development And Validation ◽  
Mean Square Errors

This study aimed to develop a new model, valid for soil with and without expandable characters, to estimate volumetric soil water content (θ) from readings of scaled frequency (SF) acquired with the Diviner 2000® sensor. The analysis was carried out on six soils collected in western Sicily, sieved at 5 mm, and repacked to obtain the maximum and minimum bulk density (ρb). During an air-drying process SF values, the corresponding gravimetric soil water content (U) and ρb were monitored. In shrinking/swelling clay soils, due to the contraction process, the variation of dielectric permittivity was affected by the combination of the mutual proportions between the water volumes and the air present in the soil. Thus, to account for the changes of ρb with U, the proposed model assumed θ as the dependent variable being SF and ρb the independent variables; then the model’s parameters were estimated based on the sand and clay fractions. The model validation was finally carried out based on data acquired in undisturbed monoliths sampled in the same areas. The estimated θ, θestim, was generally close to the corresponding measured, θmeas, with Root Mean Square Errors (RMSE) generally lower than 0.049 cm3 cm−3, quite low Mean Bias Errors (MBE), ranging between −0.028 and 0.045 cm3 cm−3, and always positive Nash-Sutcliffe Efficiency index (NSE), confirming the good performance of the model.

scholarly journals Prediksi Pengoperasian Traktor di Lahan Kering Menggunakan Artificial Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 127-133
Author(s):  
Taufik Nugraha Agassi ◽  
Yose Sebastian ◽  
Zainal Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Supervised Learning ◽  
Soil Water ◽  
Soil Water Content ◽  
Back Propagation ◽  
Learning Method ◽  
Ann Model ◽  
Dry Land

Soil water content is an important parameter in making a decision to use a tractor or not. The process of measuring soil water content and levels of field capacity in conventional which takes a long time and cannot be used in real-time to measure it is a major problem in the field. Determinants of soil water content such as ambient temperature, humidity, and rainfall can be obtained easily and quickly either by using a tool or retrieving data from the nearest BMKG station. The objective of this research is to obtain the most optimal prediction model in making decisions about tractor operation in dry land. This research uses an Artificial Neural Network (ANN) in modeling predictions of tractor operation. Prediction of tractor operation is a prediction of tractor use on a certain day using input data obtained before the day of tractor use. ANN modeling uses the back-propagation supervised learning method. The best ANN model used four hidden neurons with a learning coefficient of 0.2, a momentum of 0.8 and 20,000 iterations. This model has been able to provide optimal predictions with an accuracy value of 77%. The ANN model has been successful in predicting tractor operation on dry land using the back-propagation supervised learning method.

Calibration of Soil Water Content Data from EnviroSCAN System Using Artificial Neural Network

2016 ◽  
Vol 12 (5) ◽  
pp. 1-11
Author(s):  
Hussein Al-Ghobari ◽  
Mohamed Marazky ◽  
Abdulwahed Aboukarima ◽  
Mamdouh Minyawi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Artificial Neural

PERFORMANCE COMPARISON OF REGRESSION TECHNIQUES IN PREDICTING PARKINSON DISEASE SEVERITY SCORE USING SPEECH FEATURES

2018 ◽  
Vol 30 (04) ◽  
pp. 1850025 ◽  
Author(s):  
Savitha S. Upadhya ◽  
A. N. Cheeran
Keyword(s):  
Neural Network ◽  
Parkinson Disease ◽  
Disease Severity ◽  
Network Model ◽  
Neural Network Model ◽  
Severity Score ◽  
Performance Comparison ◽  
Disease Severity Score ◽  
Regression Techniques ◽  
Severity Of The Disease

This paper gives a performance comparison in terms of Root mean square error (RMSE) of the six regression techniques used to predict the Parkinson disease severity score. People affected by Parkinson disease suffer various muscular impairments like gait, speech etc. The severity of the disease is generally assessed by the clinicians by observing the different muscular functions of the affected people or by performing scans of the brain. This paper focusses on predicting the disease severity using features of speech signal and performing regression on these features. The features used in the prediction are the phonation features extracted from voice samples of both Parkinson disease affected people and healthy people. The 14 phonation features extracted include the frequency variability features jitter and its other variants, the energy variability features shimmer and its other variants, the mean auto correlation of the pitch frequencies, harmonicity features harmonic to noise ratio and noise to harmonic ratio. The six regression techniques used to predict the severity score are the Linear, Stepwise, Lasso, Ridge regression, prediction using Neural network model and Classification and Regression trees (CART). The trained regression model is validated using the [Formula: see text]-fold cross-validation method with [Formula: see text] values three, five, seven and ten and also using the hold out validation model in which the hold out value is taken to be 0.3. The results obtained from the six regression techniques is then compared and it shows that the severity score prediction using Neural network model provides the least RMSE of 1.5 followed by 1.8 using the CART regression technique.

Sign in / Sign up

Export Citation Format

Share Document