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The present paper reports a study, where growing hierarchical self-organising map (GHSOM) has been applied to achieve a visual cluster analysis to the Indian rainfall dataset consisting of 142 years of Indian rainfall data so that the yearly rainfall can be segregated into small groups to visualise the pattern of clustering behaviour of yearly rainfall due to changes in monthly rainfall for each year. Also, through support vector machine (SVM), it has been observed that generation of clusters impacts positively on the prediction of the Indian summer monsoon rainfall. Results have been presented through statistical and graphical analyses. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005). [ABSTRACT FROM AUTHOR]

In recent days there the farmers who are having enormous expanses of land are facing heavy loss due to sudden changes like monsoon and presence of high amount of CO2. Therefore, this article presents a Cognitive Radio (CR) model for implementing in agricultural field to predict the parameters like CO2 effect and strength of received signal. In order to monitor these parameters a new fanged technique with low cost implementation is necessary. Therefore, a low cost Effective CR Agricultural model has been incorporated by integrating the algorithms in two folds. The proposed method has the advantage that the strength of received signal will be higher even if the distance is too long. In addition the effect of CO2 will also be reduced. The projected technique is compared with other existing techniques where, the implementation cost is much lesser and also the CR model proves to be a precise technique for agricultural applications. [ABSTRACT FROM AUTHOR]

A nonparametric density estimate that incorporates spatial dependency has not been studied in the literature. In this article, we propose a new spatial density estimator that depends on two kernels: one controls the distance between observations while the other controls the spatial dependence structure. The uniform almost sure convergence of the density estimate is established with the rate of convergence. The consistency of the mode of this kernel density is also studied. Then a spatial hierarchical unsupervised clustering algorithm based on the mode estimate is presented. Some simulations as well as an application to the Monsoon Asia Drought Atlas data illustrate the efficiency of our algorithm, and a comparison of the spatial structures of these data detected by the density estimate and clustering algorithm are done. [ABSTRACT FROM AUTHOR]