Estimation of soil moisture using decision tree regression

Pekel, Engin

doi:10.1007/s00704-019-03048-8

Cited by 144 publications

(50 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We the apply regression decision trees to determine predictors and moderators for the regression models for the interactions up to the point at which the risk of deterioration of the bearing health is imminent. We are of the view that regression decision tree in the BDQRA method is applicable because the study involves continuous variables (De Cock et al, 2017, Ahmad et al, 2018, Pekel, 2020. We apply linear regression models to determine the interactions up to the point of the eminent risk of deterioration.…”

Section: How the Bdqra Methods Workmentioning

confidence: 99%

Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

Jordan

Brimicombe

2021

Preprint

View full text Add to dashboard Cite

Various data-driven methods have been applied to predict machine health indicators especially in the field of prognostics. Machine health indicators reveal the condition of equipment and/or its components including bearings by monitoring their operation data such as frequency vibration. To aid the prediction of the machine health indicators, this study applies the BDQRA method to monitor the health of bearings as a component of the machine. The BDQRA method involves applying data compression techniques like feature extraction to the bearing vibration data, to extract the most important features like time-domain, frequency domain, and time–frequency domain features. Due to the complexity of the feature extraction process, this study proposes fast Fourier transformation for the data compression. This is followed by obtaining a time series profile of the bearing vibration data to analyse the health status of component bearing. It the uses change-point analysis to predict the period at which the bearing health deterioration is imminent. Since the bearing health deterioration could be due to the independent operation of a component bearing or through communication between the component bearing and other components (or bearings) within the process machinery, the method also applies the principle of interaction effect to investigate the contributions from the other components of the machinery to the health deterioration of the component bearing detected. The accuracy of the prediction of the point of imminent health deterioration of the component bearing is investigated by comparing the outcome of the BDQRA method with the outcome of other methods published in literature which have been applied to the dataset used in this study. The findings reveal the BDQRA method have comparative advantages to the methods used in the related studies.

show abstract

Section: How the Bdqra Methods Workmentioning

confidence: 99%

Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

Jordan

Brimicombe

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Each dimension of the matrix represents the time step, the length of the rectangle and the width of the rectangle where the rectangle represents the monitoring area. The time distributed CNN is utilized to extract local features and storage temporal information as shown in Equation (5).…”

Section: B Cbgru Neural Networkmentioning

confidence: 99%

An Unsupervised PM2.5 Estimation Method With Different Spatio-Temporal Resolutions Based on KIDW-TCGRU

et al. 2020

View full text Add to dashboard Cite

The interpolation of fine-grained air quality has significant prospects in the area of air quality monitoring. The solution to this problem can effectively monitor the air quality of the areas by sparse air quality monitoring stations, so as to reduce the monitoring cost. Most of the existing researches are to solve the problem of air quality monitoring in the areas without stations by different interpolation methods. However, most of them are unable to verify the reliability of the proposed interpolation methods and can't provide a feasible range of interpolation methods at spatial resolutions. Therefore, this paper proposes an effectively unsupervised PM2.5 estimation method based on time distributed convolutional gated recurrent unit (TCGRU) and an interpolation method based on k-nearest neighbor inverse distance weighted (KIDW) to solve these problems. The model is trained by the data obtained by the interpolation method with missing one station in turn to get the monitoring capability of the areas without monitoring stations. Inverse distance weighted is combined with k-nearest neighbor to improve the performance of interpolation. In addition, time distributed convolutional neural network extracts the spatial features of air quality and storage time information for extracting temporal features by the gated recurrent units. A large number of experiments are carried out to evaluate the performance of the method by using the air quality dataset of Hubei Province, China. The experimental results show that the proposed model is effective for the monitoring of PM2.5 in the unsupervised areas and the performance (i.e. MAE=8.23, RMSE=11.27 and MAPE=19.14%) of estimating PM2.5 concentration is better than the comparative methods. INDEX TERMS Air quality modeling, spatio-temporal analysis, deep learning, inverse distance weighted, k-nearest neighbor.

show abstract

“…In this study, 2001-2011 is considered as the training period (132 monthly datasets for training) and 2012-2016 is considered as the testing period (60 monthly datasets for testing). The training dataset is subjected to 10-fold cross-validation (Waheed et al 2006;Bhuiyan et al 2019;Pekel 2020) as shown in Figure 2, panel b. In cross-validation, the training dataset is split into 10 random parts.…”

Section: Training and Testing Of Cartmentioning

confidence: 99%

“…Veettil & Mishra (2020) employed the CART for estimating the thresholds associated with climate, catchment, and morphological variables that may potentially influence the hydrological drought characteristic. Pekel (2020) applied the regression tree to estimate soil moisture considering different parameters like air temperature, time, relative humidity, and soil temperature. The applicability of CART for bias correction of satellite-based precipitation estimates remains unexplored till now on both global and regional scales.…”

Section: Introductionmentioning

confidence: 99%

Decision tree-based reduction of bias in monthly IMERG satellite precipitation dataset over India

Chaudhary

Dhanya

2020

H2Open Journal

View full text Add to dashboard Cite

Decision trees are ideally suited for handling huge datasets and modelling non-linear relationships between different variables. Given the relationship between precipitation and bias may be very complex and non-linear, bias-correction of satellite precipitation is a challenge. We examine the applicability of Classification and Regression tree (CART) for bias-correction of the Integrated Multi-satellite Retrievals for Global Precipitation Mission (IMERG) precipitation dataset over India. The gauge-based 0.25° gridded precipitation dataset from India Meteorological Department is considered as the reference. The CART model is trained (2001–2011) and tested (2012–2016) over each 0.25° grids. The training dataset is subjected to 10-fold cross-validation and optimization of the minimum size of leaf node (one of the hyper-parameter). Efficiency of the CART model is evaluated using performance metrics like R2, RMSE and MAB over entire India and different climate and elevation zones in India. CART model is observed to be highly effective in capturing the bias during the training (average R2= 0.77) and testing (average R2 = 0.66) period. Significant improvement in average monthly MAB (−6.3 to 29.2%) and RMSE (8.7–37.3%) was obtained post bias-correction by CART. Better performance of CART model was observed when compared to two widely adopted bias-correction techniques.

show abstract

Estimation of soil moisture using decision tree regression

Cited by 144 publications

References 17 publications

Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

Big Data Quantitative Risk Analysis Method for Machine Health Indicator Prediction

An Unsupervised PM2.5 Estimation Method With Different Spatio-Temporal Resolutions Based on KIDW-TCGRU

Decision tree-based reduction of bias in monthly IMERG satellite precipitation dataset over India

Contact Info

Product

Resources

About