scholarly journals Extraction of Irrigation Signals by Using SMAP Soil Moisture Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2142
Author(s):  
Liming Zhu ◽  
A-Xing Zhu
Keyword(s):  
Soil Moisture ◽  
Environmental Factors ◽  
Spatial Pattern ◽  
Potential Evapotranspiration ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Irrigation Frequency ◽  
Growth Season ◽  
Fuzzy Membership Functions ◽  
Relevant Degree

To allow extraction of irrigation signals from satellite-derived data on soil moisture, this study describes the development of an irrigation signal extraction method that takes into account multiple environmental factors in irrigation. Firstly, the fuzzy membership functions of irrigation relating to multiple environmental factors are constructed. Then, a model is built based on the fuzzy membership functions by using operation rules of fuzzy sets, which is used to infer the relevant degree of irrigation to nonirrigation. Finally, the irrigation signals in satellite-based soil moisture data are recognized according to the relevant degree. Taking Henan Province in the North China Plain as the study area, the proposed method is used to extract irrigation signals from the SMAP Level 3 Passive Soil Moisture Product. Extracted irrigation signals from two SMAP grids are validated using daily in situ soil moisture and precipitation data, with the results showing correct identification of most of the irrigation signals. By grading the membership degree of the extracted irrigation signals, irrigation frequency maps for the 2016–2017 winter crop growth season and the 2017 summer crop growth season are obtained for Henan Province. Compared to the irrigation frequency maps with data on the annual precipitation and the annual potential evapotranspiration, the irrigation frequency maps show a spatial pattern opposite that of the annual precipitation and a spatial pattern similar to that of the annual potential evapotranspiration. It is common sense that areas with low precipitation and high evapotranspiration need more irrigation frequency and irrigation water. Thus, the spatial patterns of irrigation frequency maps are reasonable in a sense. However, it should be noted that the observed irrigation data used in the qualitative assessments are rendered less convincing by the SMAP product’s coarse resolution. Quantitative validation of extracted irrigation signals remains a significant challenge, and small-scale irrigation cannot be captured by coarse-resolution satellite-based soil moisture products. Thus, a high-resolution soil moisture product should be used to extract irrigation signals in future.

scholarly journals Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

2021 ◽  
Author(s):  
Jia-Bin Zhou ◽  
Yan-Qin Bai ◽  
Yan-Ru Guo ◽  
Hai-Xiang Lin
Keyword(s):  
Support Vector Machine ◽  
Negative Impact ◽  
Twin Support Vector Machine ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Intuitionistic Fuzzy ◽  
Benchmark Datasets ◽  
The Impact

AbstractIn general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.

A method for automatic generation of fuzzy membership functions for mobile device’s characteristics based on Google Trends

2013 ◽  
Vol 29 (2) ◽  
pp. 510-517 ◽  
Author(s):  
Aitor Almeida ◽  
Pablo Orduña ◽  
Eduardo Castillejo ◽  
Diego López-de-Ipiña ◽  
Marcos Sacristán
Keyword(s):  
Automatic Generation ◽  
Google Trends ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Fuzzy membership functions of procurement selection criteria

2002 ◽  
Vol 20 (3) ◽  
pp. 285-296 ◽  
Author(s):  
S. Thomas Ng ◽  
Duc Thanh Luu ◽  
Swee Eng Chen ◽  
Ka Chi Lam
Keyword(s):  
Selection Criteria ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

scholarly journals The Performance of Stock Portfolios formed using Fuzzy Logic

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Amit K. Sinha 1 ◽  
Andrew J. Jacob 2
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Fuzzy Membership ◽  
Thought Process ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Stock Portfolios ◽  
Financial Securities

Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert’s thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and applied to form portfolios. Our results indicate that this approach to form stock portfolios can result in superior returns than the market as measured by the return on the S&P 500. These portfolios may also provide superior risk-adjusted returns when compared to the market.

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