In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.
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.KEY WORDS: soil texture, particle-size distribution, data-driven modeling, ensemble model, random forests ACADEMIC EDITOR: carlos Alberto Martinez-huitle, editor is chief PEER REVIEW: three peer reviewers contributed to the peer review report. reviewers' reports totaled 993 words, excluding any confidential comments to the academic editor.
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.
The problem of drought probability has been investigated by several authors, who have usually analysed droughts using various drought indices such as the Standard Precipitation Index. Various aspects of time series of such indices (intensity, severity and duration) were investigated by several authors using a copula method. Because such analysis is based on only one basic climatic variable, this paper addresses a different approach, i.e., joint analysis of the severity and duration of the most demanding potential annual irrigation periods by a bivariate copula method. Characteristics of these periods are derived from both temperature and precipitation. Maximum annual duration of the potential irrigation period and corresponding rainfall deficit were inferred from these basic variables as inputs to two-dimensional probability analysis by the copula method, because this offers more direct answers to questions of irrigation needs. Results indicate the suitability of the proposed method for analysis of irrigation needs, with greater benefits than the typical one-dimensional analysis of individual climatic variables. A case study for testing the method was done for southwestern Slovakia, for which the frequency of irrigation needs was estimated. Example results indicate that every second year, a one-month period can be expected in which temperatures are > 25 • C and there is a moisture deficit of ∼30 mm. Even more significant periods of drought can be expected, for example, with a 5 or 10-year return period. These phenomena significantly damage agriculture yields, so requirements for irrigation structures in the study area are indicated by the proposed method.
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 networkbased model. The testing accomplished shows that the RF algorithm performs with a higher degree of accuracy and is more suitable for this task.
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