Application of simple clustering on space‐time mapping of mean monthly rainfall pattern

Nasseri, Mohsen; Zahraie, Banafsheh

doi:10.1002/joc.2109

Cited by 23 publications

(10 citation statements)

References 29 publications

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“…Various methods have been employed to overcome these problems. Among them, clustering methods attain more accuracy in estimation by taking into account data similarities (Abedini et al 2008;Nasseri and Zahraie 2010). Research indicated that clustering methods reduce estimation error variance and mean square error and thus improve the accuracy of results for estimating spatial and spatiotemporal patterns of precipitations (Abedini et al 2008;Nasseri and Zahraie 2010).…”

Section: Introductionmentioning

confidence: 98%

“…Among them, clustering methods attain more accuracy in estimation by taking into account data similarities (Abedini et al 2008;Nasseri and Zahraie 2010). Research indicated that clustering methods reduce estimation error variance and mean square error and thus improve the accuracy of results for estimating spatial and spatiotemporal patterns of precipitations (Abedini et al 2008;Nasseri and Zahraie 2010). Abedini et al (2008) coupled accelerated exact k-mean with the OK method to improve the accuracy of the OK method by clustering piezometric head patterns in west Texas (USA).…”

Section: Introductionmentioning

confidence: 98%

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Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach

2014

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The main purpose of this research is to investigate spatial variations of mean annual precipitation in a watershed. As a case study, the research focused on the Namak Lake watershed in Iran. Literature provides various techniques for studying spatial patterns of precipitation in a watershed. These techniques often require a large dataset. On the other hand, nonuniform data distribution in a watershed can reduce the accuracy and reliability of the predictions. To overcome these problems, this research applied the cluster method coupled with ordinary Kriging and Bayesian maximum entropy techniques. An estimated point was modified based on the distance from the point to the cluster center. The research considered elevation variations as a secondary variable. A cross-validation technique was used for evaluating the results of mean annual precipitations. The research compared the results of ordinary Kriging and Bayesian maximum entropy methods with and without the application of the clustering method. The research concluded that the cluster-based method can estimate the dynamics of long-term mean annual precipitation more reliably and accurately. The research also revealed more informative results for the cluster-based method.Résumé : Le but principal de la présente recherche était d'étudier les variations spatiales de la précipitation moyenne annuelle dans un bassin versant. Pour l'étude, la recherche ciblait le bassin versant du lac Namak en Iran. Il existe, dans la littérature, diverses techniques pour étudier les configurations spatiales de la précipitation dans un bassin versant. Ces techniques exigent souvent un vaste ensemble de données. Par contre, la distribution non uniforme des données dans un bassin versant peut réduire la précision et la fiabilité des prévisions. Afin de surmonter ces problèmes, la présente recherche a appliqué la méthode de grappes jumelée à des techniques de krigeage ordinaire et d'entropie maximale bayésienne. Un point estimé était modifié en se basant sur la distance entre ce point et le centre de la grappe. Les variations d'élévation étaient considérées des variables secondaires. Une technique de validation croisée a été utilisée pour évaluer les résultats des précipitations annuelles moyennes. La recherche comparait les résultats des méthodes de krigeage ordinaire et d'entropie maximale bayésienne avec et sans l'application de la méthode des grappes. La recherche conclut que la méthode basée sur les grappes peut estimer la dynamique de précipitation moyenne annuelle à long terme avec une meilleure fiabilité et plus de précision. La recherche révèle aussi plus d'information sur les résultats obtenus par la méthode basée sur les grappes. [Traduit par la Rédaction]

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 98%

Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach

2014

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“…To address such challenges in these areas, the literature accentuates the importance of adopting more water-saving technologies through the efficient storage and use of water [11]. Several studies described STRV on different scales [12][13][14][15][16]; however, these studies rarely demonstrated the potential relationship between STRV and yield variability among farmer fields located within the same agricultural watershed. Rainfall studies in the forms of trend analyses and spatial variability over large areas are numerous, but these studies have limited connections to local agricultural challenges.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Intra and Interseasonal Rainfall Variability and Its Effects on Pearl Millet Yield in a Semiarid Agroclimate: Significance of Scattered Fields and Tied Ridges

Silungwe

Graef

Bellingrath-Kimura

et al. 2019

Water

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Establishing food security in sub-Saharan African countries requires a comprehensive and high resolution understanding of the driving factors of crop production. Poor soil and adverse climate conditions are among the major drivers of poor regional crop production. Drought and rainfall variability challenges are not fully being addressed by rainfed producers in semiarid areas. In this study, we analysed the spatiotemporal rainfall variability (STRV) and its effects on pearl millet yield using two seasons of data collected from 38 rain gauge stations scattered randomly in farm plots within a 1500 ha area of semiarid central Tanzania. The STRV effects on pearl millet yield under flat and tied ridge management were analysed. Our results show that seasonal rainfall can vary significantly for neighboring fields at distances of less than 200 m, which impacts yield. The STRV for daily rainfall was found to be more critical than for total seasonal rainfall amounts. Scattering fields can help farmers avoid total harvest loss by obtaining at least some yield from the areas that received adequate rain. The use of tied ridges is recommended to conserve soil moisture and improve yields more than flat cultivation in semiarid areas.

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“…have been widely used to identify the homogenous regions. Some specific applications of cluster methods include the identification of homogeneous regions for regional flood frequency analysis (Burn, 1989;Burn and Goel 2000;Lecce, 2000;Thandaveswara and Sajikumar, 2000), defining of the homogeneous precipitation regions (Smithers and Schulze, 2001;Nasseri and Zahraie, 2011). Data sets are separated into subregions with similar characteristics by clustering analysis and more sensitive estimation models are set up for these sub-regions.…”

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confidence: 99%

Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods

Aydoğdu

Fırat

2014

Water Resour Manage

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In this study, a novel approach combining fuzzy clustering and Least Squares Support Vector machine (LS-SVM) methods is developed for estimation of failure rate in water distribution networks and for determination of the relationship between failure rateeffective factors. For this aim, failure data observed Malatya water distribution network during 2006-2012 was selected as study area. In first phase, estimation model was developed and tested for the complete data set in estimating the failure rate by LS-SVM method. Then, in order to develop a more sensitive estimation model and to improve the performance of LS-SVM, 9 sub-regions were defined with similar characteristics by using fuzzy clustering method. Then failure rate estimation was carried out for each of the sub-regions using by LS-SVM method. Feed Forward Neural Network (FFNN) and Generalized Regression Neural Network (GRNN) methods were also used for estimation of failure rate and the results were compared with those of LS-SVM. The criteria such as Correlation Coefficient (R), Efficieny (E) and Root Mean Square Error (RMSE) were used to evaluate the performance of models. The results showed that LS-SVM model gives better results in comparison with the FFNN and GRNN models. It was also determined that LSSVM model results for the sub-regions defined by clustering analysis are better and that the clustering analysis increases the estimation model performance in addition to the fact that the estimation results have become better. In conclusion, it can be possible to develop a more sensitive estimation models using fuzzy clustering and LSSVM methods.

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Application of simple clustering on space‐time mapping of mean monthly rainfall pattern

Cited by 23 publications

References 29 publications

Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach

Improving Bayesian maximum entropy and ordinary Kriging methods for estimating precipitations in a large watershed: a new cluster-based approach

Analysis of Intra and Interseasonal Rainfall Variability and Its Effects on Pearl Millet Yield in a Semiarid Agroclimate: Significance of Scattered Fields and Tied Ridges

Estimation of Failure Rate in Water Distribution Network Using Fuzzy Clustering and LS-SVM Methods

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