Geostatistical modeling to simulate daily rainfall variability in Iran

Javari, Majid

doi:10.1080/23312041.2017.1416877

Cited by 14 publications

(5 citation statements)

References 55 publications

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“…The application of the numeric threshold distance adjacency matrix mentioned in Section 3.2.2 as the numeric-type spatial weight matrix requires us to determine an appropriate threshold distance to reflect the real changes in spatial phenomena before the experiments. Incremental spatial autocorrelation analysis refers to the calculation of the global spatial autocorrelation for a series of increasing distances and measurement of the intensity of spatial clustering for each distance based on the z-score returned [46]. The z-score generally peaks, which reflects those distances where the spatial processes promoting clustering are the most pronounced.…”

Section: Numerical Distance Type Fusion Process and Results Analysismentioning

confidence: 99%

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Wang

Che³

et al. 2021

IJGI

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Due to the increasingly complex objects and massive information involved in spatial statistics analysis, least squares support vector regression (LS-SVR) with a good stability and high calculation speed is widely applied in regression problems of geospatial objects. According to Tobler’s First Law of Geography, near things are more related than distant things. However, very few studies have focused on the spatial dependence between geospatial objects via SVR. To comprehensively consider the spatial and attribute characteristics of geospatial objects, a geospatial LS-SVR model for geospatial data regression prediction is proposed in this paper. The 0–1 type and numeric-type spatial weight matrices are introduced as dependence measures between geospatial objects and fused into a single regression function of the LS-SVR model. Comparisons of the results obtained with the proposed and conventional models and other traditional models indicate that fusion of the spatial weight matrix can improve the prediction accuracy. The proposed model is more suitable for geospatial data regression prediction and enhances the ability of geospatial phenomena to explain geospatial data.

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Section: Numerical Distance Type Fusion Process and Results Analysismentioning

confidence: 99%

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Wang

Che³

et al. 2021

IJGI

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“…In addition, rainfall estimation is of fundamental importance during hydrological modeling, models that have a high level of reliability of these estimates are required, as these can be used to prevent flooding and to assess runoff of water quality and ecosystem health in urbanized The geostatistical methodology presented in this study can, in principle, be used for urban and catchment interpolation. However, when using analysis for the spatial distribution of precipitation data on a daily scale, geostatistical methods present several difficulties, mainly in the presence of large temporal variability (Javari 2017) and spatial variability, as it increases the complexity of estimates on the uptake scale (Knight et al 2005). However, once considered that the variable presents an irregular behavior, these difficulties can be considered in the spatio-temporal geostatistics, because this methodology, it is possible to enter autocorrelation structures space-time capable, such as those used in this manuscript, to incorporate modeling the occurrence of high rainfall variability.…”

Section: Discussionmentioning

confidence: 99%

Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends

Medeiros

Lima

Olinda

et al. 2022

An. Acad. Bras. Ciênc.

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The main objective of this study is to propose different probabilistic models for adjusting the trend component, since it significantly influences the quality of the spatiotemporal interpolation of rainfalls. We used the monthly total precipitation data of the São Francisco River Basin (SFRB) for the period of 31 years, 1989-2019. The SFRB occupies 8% of the whole Brazilian territory, mostly located in the Northeast Brazilian region. For the trend component, we propose the fitted GAMLSS models by comparing different probability distribution families, which in most cases include the characteristics of these data. The results indicate the existence of a spatio-temporal pattern of the residues obtained from the adjustment of the trend with zero adjusted Gamma distribution for the accumulated monthly precipitation. The adjustment revealed a spatial dependence of up to 873 km between the pluviometric stations and temporal autocorrelation of approximately 1.6 months. The methodology used in this study enabled us to create rainfall maps, interpolating unobserved locations in differences years. The projection of these maps to the SFRB is considered extremely important for planning and implementing activities related to water resources across the river basin.

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“…Temporal data and spatially predicted data for global temperature and precipitation were defined using data reanalyzed by NCEP/NCAR and data extracted by ArcGIS Pro3 and MATLAB software, covering the reference period of 1991-2020. Geostatistical and statistical methods were used to predict spatiotemporal variability and examine the regionalization of temperature and precipitation changes worldwide [1] . Temporal and predicted world temperature and precipitation datasets have been generated based on reanalyzed data from NCEP/NCAR and extracted data from ArcGIS Pro3 for the reference period of 1991-2020 ( Fig.…”

Section: Data Descriptionmentioning

confidence: 99%

Geostatistical modeling to simulate daily rainfall variability in Iran

Cited by 14 publications

References 55 publications

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Geospatial Least Squares Support Vector Regression Fused with Spatial Weight Matrix

Performance assessment of spatio-temporal regression kriging with GAMLSS models as trends

Datasets for the comparison of monthly spatiotemporal precipitation and temperature changes in a predicted GeoS-NCEP/NCAR-ANN dataset

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