An interpretable fuzzy monthly rainfall spatial interpolation system for the construction of aerial rainfall maps

Kajornrit, Jesada; Wong, Kok Wai; Fung, Chun Che

doi:10.1007/s00500-014-1456-9

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…The methods such as support vector machine [7] and artificial neural network (ANN) [8] were used, and the methods need no prior knowledge and assumptions. A Fuzzy system was introduced to consider the interpretability of the spatial interpolation models [9]. The interpolation method is more diverse and can be selected according to different watershed conditions.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling

Chen

Ren

Yuan

et al. 2017

Water

103

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Abstract:The spatial distribution of precipitation is an important aspect of water-related research. The use of different interpolation schemes in the same catchment may cause large differences and deviations from the actual spatial distribution of rainfall. Our study analyzes different methods of spatial rainfall interpolation at annual, daily, and hourly time scales to provide a comprehensive evaluation. An improved regression-based scheme is proposed using principal component regression with residual correction (PCRR) and is compared with inverse distance weighting (IDW) and multiple linear regression (MLR) interpolation methods. In this study, the meso-scale catchment of the Fuhe River in southeastern China was selected as a typical region. Furthermore, a hydrological model HEC-HMS was used to calculate streamflow and to evaluate the impact of rainfall interpolation methods on the results of the hydrological model. Results show that the PCRR method performed better than the other methods tested in the study and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. Simulated streamflow showed different characteristics based on the mean, maximum, minimum, and peak flows. The results simulated by PCRR exhibited the lowest streamflow error and highest correlation with measured values at the daily time scale. The application of the PCRR method is found to be promising because it considers multicollinearity among variables.

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Section: Introductionmentioning

confidence: 99%

Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling

Chen

Ren

Yuan

et al. 2017

Water

103

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“…Hence, the machine learning algorithm is effective for rainfall spatial estimation with its complex influencing factors and vague physical mechanism. At present, machine learning algorithms such as the artificial neural network (ANN) [34,38,80,81], association rule mining [82], fuzzy inference [83], and random forest [84] have been used in rainfall spatial interpolation and have had some success.…”

Section: Machine Learningmentioning

confidence: 99%

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

Wang

et al. 2019

Water

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Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.

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“…System modeling techniques that employ fuzzy set theory are commonly referred to as fuzzy modeling [15,18,19]. One of the most commonly used fuzzy modeling techniques for spatial estimation is the Takagi-Sugeno (TS) method [15,18,20].…”

Section: The Takagi-sugeno Methodsmentioning

confidence: 99%

“…For spatial modeling, data are clustered in the three-dimensional product space defined by the Cartesian map coordinates (x, y) and the magnitude of a pollutant concentration (p) (see Figure 2). The Takagi-Sugeno Method System modeling techniques that employ fuzzy set theory are commonly referred to as fuzzy modeling [15,18,19]. One of the most commonly used fuzzy modeling techniques for spatial estimation is the Takagi-Sugeno (TS) method [15,18,20].…”

Section: The Takagi-sugeno Methodsmentioning

confidence: 99%

An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data

Thomas

Khan²,

Valeo

et al. 2021

Environments

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Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects.

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An interpretable fuzzy monthly rainfall spatial interpolation system for the construction of aerial rainfall maps

Cited by 10 publications

References 22 publications

Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling

Comparison of Spatial Interpolation Schemes for Rainfall Data and Application in Hydrological Modeling

Rainfall Spatial Estimations: A Review from Spatial Interpolation to Multi-Source Data Merging

An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data

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