Conversion of the Time Series of Measured Soil Moisture Data to a Daily Time Step – A Case Study Utilizing the Random Forests Algorithm

Modeling the water content in soil is important for the development of agricultural information systems. Various data are necessary for such modelling. In this paper the authors are proposing a methodology for a frequent situation, i.e., when the modeler is facing a problem due to the lack of available data. Soil water prediction, e.g., for irrigation planning, should be performed with a daily time step. Unfortunately, past measurements of soil moisture, which are necessary for the calibration of a model, are often not available at such a frequency. In the case study presented the soil moisture data were acquired every two weeks. The authors have tested a model utilizing the Random Forests (RF) algorithm, which was used for the conversion of the original data to data with a daily time step. The accuracy of the application of RF to this task is compared with a neural network-based model. The testing accomplished shows that the RF algorithm performs with a higher degree of accuracy and is more suitable for this task

Using R in Water Resources Education

This review paper will deal with the possibilities of applying the R programming language in water resources and hydrologic applications in education and research. The objective of this paper is to present some features and packages that make R a powerful environment for analysing data from the hydrology and water resources management fields, hydrological modelling, the post processing of the results of such modelling, and other task. R is maintained by statistical programmers with the support of an increasing community of users from many different backgrounds, including hydrologists, which allows access to both well established and experimental techniques in various areas

River Flows Prediction By Ensemble Model

This review paper will deal with the possibilities of applying the R programming language in water resources and hydrologic applications in education and research. The objective of this paper is to present some features and packages that make R a powerful environment for analyzing data from the hydrology and water resources management fields, hydrological modelling, the post-processing of the results of such modelling, and other tasks

Conversion Between Soil Texture Classification Systems Using the Random Forest Algorithm

This study focuses on the reclassification of a soil texture system following a hybrid approach in which the conventional particle-size distribution (PSD) models are coupled with a random forest (RF) algorithm for achieving more generally applicable and precise outputs. The existing parametric PSD models that could be used for this purpose have various limitations; different models frequently show unequal degrees of precision in different soils or under different environments. The authors present in this article a novel ensemble modeling approach in which the existing PSD models are used as ensemble members. An improvement in precision was proved by better statistical indicators for the results obtained, and the article documents that the ensemble model worked better than any of its constituents (different existing parametric PSD models). This study is verified by using a soil dataset from Slovakia, which was originally labeled by a national texture classification system, which was then transformed to the USDA soil classification system. However, the methodology proposed could be used more generally, and the information provided is also applicable when dealing with the soil texture classification systems used in other countries