Optimal functional forms for estimation of missing precipitation data

Teegavarapu, Ramesh S. V.; Tufail, Muhammad; Ormsbee, Lindell

doi:10.1016/j.jhydrol.2009.06.014

Cited by 67 publications

(34 citation statements)

References 23 publications

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“…The results obtained in evaluating the IDW method's capability for correctly estimating measured rainfall are in line with the findings by Xia et al (1999), Bennett et al (2007) and Teegavarapu et al (2009), which show that IDW estimates rainfall well and is a recommended method of filling in the missing rainfall data. According to these researchers, the method performs best if the density of the stations is high.…”

supporting

confidence: 87%

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Moeletsi¹,

Shabalala²,

Nysschen³

et al. 2016

WSA

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Climate data recorded by national meteorological agencies is either incomplete or faulty for some periods due to a number of reasons. Multi-functional utilization of climate data in complete form necessitates the filling of these gaps. In this study an inverse distance weighting (IDW) method was used to estimate rainfall utilizing neighbouring station data in the Free State Province of South Africa. Six weather stations evenly distributed across the province, and with data for 1950 to 2008, were used to evaluate this patching IDW approach at daily and dekadal time steps. Coefficient of determination (r 2 ), mean absolute error (MAE) and mean bias error (MBE) were the statistics used in the assessment. Firstly, the study conducted a sensitivity analysis of the IDW exponent (p) which showed that the best results are obtained when p is either 2 or 2.5. The estimated values at all six stations were highly correlated with the measured rainfall data with an overall r 2 value exceeding 0.70 for both daily and dekadal estimates. MAE showed low miscalculations with values with an average of 1 mm per day and 4.4 mm per dekad. MBE was very low for both daily and dekadal evaluations but the disaggregated data showed underestimation of the IDW mostly for daily rainfall exceeding 10 mm. Thus, IDW methodology proved to be an acceptable approach for estimating both daily and dekadal rainfall in the Free State Province.

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supporting

confidence: 87%

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Moeletsi¹,

Shabalala²,

Nysschen³

et al. 2016

WSA

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“…Chandramouli (2005) used ANNs, andTeegavarapu (2007) demonstrated the use of universal functional approximation within a stochastic variance dependent interpolation technique for estimation of missing precipitation data. Recent work of Teegavarapu et al (2009) focused on the development of optimal functional forms for estimating missing precipitation data. The methods using optimal forms provided better estimates compared to those by traditional distance-based methods.…”

Section: Introductionmentioning

confidence: 99%

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Teegavarapu

2012

Hydrological Sciences Journal

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Editor D. Koutsoyiannis; Associate editor S. Grimaldi Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383-406. Abstract New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some naïve approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study.Key words missing precipitation data; spatial interpolation; genetic algorithms; nonlinear optimization methods; mixed integer mathematical programming (MINLP), multiple linear regression, nonlinear least squares approach; thin-plate splines, trend surface models, Kentucky, USA Interpolation spatiale à l'aide de modèles non linéaires de programmation mathématique pour l'estimation de précipitations manquantes Résumé Nous proposons ici des modèles de programmation mathématique innovants et originaux, développés et évalués pour estimer des données de précipitations manquantes. Ces modèles utilisent les formulations de la programmation mathématique non linéaire et non linéaire mixte en nombres entiers binaires, qui surmontent les limites des méthodes d'interpolation spatiale où le choix des paramètres de pondération est arbitraire, de même que le nombre de points de contrôle au sein d'un voisinage et que le voisinage lui-même. Les formulations sont établies en utilisant les algorithmes génétiques. Les données de précipitations quotidiennes provenant de 15 stations pluviométriques d'une région climatique tempérée ont été utilisées pour tester et évaluer l'efficacité de ces méthodes. Elles ont été comparées à quelques approches classiques : la régression linéaire multiple, l'optimisation non linéaire selon les moindres carrés, le krigeage, la surface tendancielle globale ou locale et les fonctions splines. Les résultats suggèrent que la proposition de nouvelles formulations de programmation mathématique fournit de meilleurs résultats que ceux obtenus à partir de toutes les autres méthodes d'interpolation spatiale testées dans cette étude.Mots clefs données de précipitations manq...

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“…Applications of artificial neural networks (ANNs) for estimation of missing precipitation data were documented by Teegavarapu and Chandramouli (2005) and Teegavarapu (2007). Recent work of Teegavarapu et al (2009) has focused on the development of optimal functional forms using genetic algorithms and mathematical operators for estimating missing precipitation data. The functional forms provided better estimates compared to those by traditional geographical distance-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…Limitations of spatial interpolation methods when used for estimation of missing data are documented by Teegavarapu (2008Teegavarapu ( , 2009) and Teegavarapu et al (2009). Reciprocal distances as weights in many interpolation methods may not serve as surrogate measures for spatial correlation.…”

Section: Introductionmentioning

confidence: 99%

Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods

Teegavarapu

2014

Hydrological Sciences Journal

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To cite this article: Ramesh S. V. Teegavarapu (2014) Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods, Hydrological Sciences Journal, 59:11, 200959:11, -202659:11, , DOI: 10.108059:11, /02626667.2013 To link to this article: https://doi.org/10.1080/02626667.2013.862334 Abstract New optimal proximity-based imputation, K-nearest neighbour (K-NN) classification and K-means clustering methods are proposed and developed for estimation of missing daily precipitation records. Mathematical programming formulations are developed to optimize the weighting, classification and clustering schemes used in these methods. Ten different binary and real-valued distance metrics are used as proximity measures. Two climatic regions, Kentucky and Florida, (temperate and tropical) in the USA, with different gauge density and network structure, are used as case studies to evaluate the new methods. A comprehensive exercise is undertaken to compare the performances of the new methods with those of several deterministic and stochastic spatial interpolation methods. The results from these comparisons indicate that the proposed methods performed better than existing methods. Use of optimal proximity metrics as weights, spatial clustering of observation sites and classification of precipitation data resulted in improvement of missing data estimates.

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Optimal functional forms for estimation of missing precipitation data

Cited by 67 publications

References 23 publications

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Evaluation of an inverse distance weighting method for patching daily and dekadal rainfall over the Free State Province, South Africa

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods

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