A Data Mining System for Real Time Soil Moisture Prediction

Matei, Oliviu; Rusu, Teodor; Petrovan, Adrian; Mihuţ, Gabriel

doi:10.1016/j.proeng.2017.02.475

Cited by 60 publications

(26 citation statements)

References 13 publications

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“…Costache et al [26] successfully used the gradient boosting trees (GBT) and multilayer perceptron (MLP) method to evaluate the flood potential and to predict flood sensitive areas in the Trotus river basin in Romania. Matei et al [27,28] and Anton et al [29] used various techniques, such as collaborative or context-aware data mining, for predicting the soil moisture in Transylvania, Romania. Wu et al [30] used the gradient boosting decision tree (GBDT) algorithm to predict urban floods in Zhengzhou City.…”

Section: Introductionmentioning

confidence: 99%

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

et al. 2020

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The temperature of the soil at different depths is one of the most important factors used in different disciplines, such as hydrology, soil science, civil engineering, construction, geotechnology, ecology, meteorology, agriculture, and environmental studies. In addition to physical and spatial variables, meteorological elements are also effective in changing soil temperatures at different depths. The use of machine-learning models is increasing day by day in many complex and nonlinear branches of science. These data-driven models seek solutions to complex and nonlinear problems using data observed in the past. In this research, decision tree (DT), gradient boosted trees (GBT), and hybrid DT–GBT models were used to estimate soil temperature. The soil temperatures at 5, 10, and 20 cm depths were estimated using the daily minimum, maximum, and mean temperature; sunshine intensity and duration, and precipitation data measured between 1993 and 2018 at Divrigi station in Sivas province in Turkey. To predict the soil temperature at different depths, the time windowing technique was used on the input data. According to the results, hybrid DT–GBT, GBT, and DT methods estimated the soil temperature at 5 cm depth the most successfully, respectively. However, the best estimate was obtained with the DT model at soil depths of 10 and 20 cm. According to the results of the research, the accuracy rate of the models has also increased with increasing soil depth. In the prediction of soil temperature, sunshine duration and air temperature were determined as the most important factors and precipitation was the most insignificant meteorological variable. According to the evaluation criteria, such as Nash-Sutcliffe coefficient, R, MAE, RMSE, and Taylor diagrams used, it is recommended that all three (DT, GBT, and hybrid DT–GBT) data-based models can be used for predicting soil temperature.

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Section: Introductionmentioning

confidence: 99%

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

et al. 2020

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“…ANN models have been widely used in the development of PTFs (Matei et al, 2017;Pachepsky et al, 1996;Schaap et al, 2001;Zhang et al, 2018;Zhang and Schaap, 2017). ANNs are universal approximators that can approximate any nonlinear mapping.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques

Araya¹,

Fryjoff-Hung²,

Anderson³

et al. 2020

Preprint

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Abstract. We developed machine learning models to retrieve surface soil moisture (0–4 cm) from high resolution multispectral imagery using terrain attributes and local climate covariates. Using a small unmanned aircraft system (UAS) equipped with a multispectral sensor we captured high resolution imagery in part to create a high-resolution digital elevation model (DEM) as well as quantify relative vegetation photosynthetic status. We tested four different machine learning algorithms. The boosted regression tree algorithm gave the best prediction with mean absolute error of 3.8 % volumetric water content. The most important variables for the prediction of soil moisture were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing data and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

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“…O. Matei et al in [11] present a data mining system capable of gathering weather data from several weather stations and predicting the moisture of the soil for the next day, all in real time. The system has been tested in real-life conditions, in Transylvanian Plain, with ten weather stations.…”

Section: Collaborative Data Miningmentioning

confidence: 99%

Survey of Collaborative Data Mining

Anton

Matei

Avram

2019

Carpathian Journal of Electronic and Computer Engineering

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In the information industry there is a huge amount of data which can be used to obtain relevant information necessary for them to be processed, cleaned and analyzed. This survey paper presents different data extraction research that can be applied in various areas. The main point is to determine a new approach and to be the starting point for new experiments in agriculture predictions. The collaborative data mining approach assumes that correlated units or devices will have similar behavior that can be determined with an acceptable approximation.

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A Data Mining System for Real Time Soil Moisture Prediction

Cited by 60 publications

References 13 publications

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

Soil Temperature Estimation with Meteorological Parameters by Using Tree-Based Hybrid Data Mining Models

Advances in Soil Moisture Retrieval from Multispectral Remote Sensing Using Unmanned Aircraft Systems and Machine Learning Techniques

Survey of Collaborative Data Mining

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