Ensemble Prediction of Stream Flows Enhanced by Harmony Search Optimization

This work presents the application of a data-driven model for streamflow predictions, which can be one of the possibilities for the preventive protection of a population and its property. A new methodology was investigated in which ensemble modeling by data-driven models was applied and in which harmony search was used to optimize the ensemble structure. The diversity of the individual basic learners which form the ensemble is achieved through the application of different learning algorithms. In the proposed ensemble modeling of river flow predictions, powerful algorithms with good performances were used as ensemble constituents (gradient boosting machines, support vector machines, random forests, etc.). The proposed ensemble provides a better degree of precision in the prediction task, which was evaluated as a case study in comparison with the ensemble components, although they were powerful algorithms themselves. For this reason, the proposed methodology could be considered as a potential tool in flood predictions and prediction tasks in general

Support Of Teaching And Research In Hydroinformatics With R

R is a free software programming language and environment for statistical computing and graphics. Polls and surveys show that R\u27s popularity has increased substantially in recent years. The objective of this paper is to review features that make R a powerful environment for pre-processing and analyzing data from hydrology and water resources management, and for various other tasks such as hydrological processes modeling, time series analysis, trend analysis, GIS analysis of the watershed, geostatistics, extreme value analysis and various other tasks. This review paper will deal with the possibilities of applying the R programming language in water resources and hydrologic applications in education and research. Its possibility of extension is widely used by R users from many different backgrounds. Consequently this leads to one of the best things about R, which is the large amount of existing add-ins (so-called “packages”), which are aimed at solving various tasks in different fields including hydrology, water resources and meteorology. Authors would like to stress, that a tool as R is very useful, e.g., in the process of learning some difficult subject related to an analysis of hydrological data (e.g., copulas). In R one has possibility of easily trying corresponding computations, which are otherwise only described by complicated theories. Of course it is necessary to know the background of computations, but it is very helpful in the process of learning some intimidating and complicated subject, if one knows that he can do the very thing which is trying to understand

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

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

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

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