Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

Krishna, G. Vamsi

doi:10.5815/ijmsc.2015.01.02

Cited by 8 publications

(3 citation statements)

References 14 publications

(3 reference statements)

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“…Understanding the significant parameters contributing to the classification of precipitation is essential for accurate analysis and forecasting. Predicting rainfall has become increasingly important due to its impact on various sectors, such as agriculture [8], aquaculture [9], and the economy [10]. Factors such as the area where rainfall occurs, global heat, and indirect parameters associated with rainfall make it necessary to effectively predict rainfall from satellite images [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rainfall Trends using Mahalanobis-Taguchi System

Mohd Jamil,

Abu,

Mohd Zaini

et al. 2024

J. Eng. Technol. Sci.

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Full comprehension of precipitation patterns is crucially needed, especially in Pekan, a district in Pahang, Malaysia. The area is renowned for its elevated levels of precipitation, making it imperative to precisely categorize and enhance the analysis of rainfall patterns to facilitate effective resource allocation, agricultural productivity, and catastrophe readiness. The variability of rainfall patterns is contingent upon geographical location, necessitating the collection of a comprehensive data set that includes several characteristics that influence precipitation to make reliable predictions. Data were collected from the Vantage Pro2 weather station, which is located on the UMP Pekan campus. This study used the RT method to classify rainfall and T-Method 1 to determine the degree of contribution of each parameter. Significant parameters were validated using a data set from the same type of weather station but in a different district. The results showed that the Mahalanobis-Taguchi Bee Algorithm (MTBA) is more effective than the Mahalanobis-Taguchi System (MTS) in finding the significant parameters, but the parameters were a subset of MTS Teshima. Finally, the validation with T mean-based error (Tmbe) using Mean Absolute Error (MAE) revealed a pattern of errors to provide insight to find the significant parameters of MTS.

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Section: Introductionmentioning

confidence: 99%

Prediction of Rainfall Trends using Mahalanobis-Taguchi System

Mohd Jamil,

Abu,

Mohd Zaini

et al. 2024

J. Eng. Technol. Sci.

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“…Apache Hadoop distribution is one of the clusters frameworks in distributed environment that helps by distributing voluminous data across a number of nodes in the framework and focused on Map-Reduce design and implementation of Apriori algorithm for structured data analysis. G.Vamsi Krishna [16] proposed a method based on Gaussian Mixture model together with K-means clustering to predict rainfall with many associated factors. Deepa B. Patila and Yashwant V. Dongre [17] performed a comparative study on fuzzy c-means and k-means clustering and stated that fuzzy clustering are more appropriate for document clustering.…”

Section: Related Workmentioning

confidence: 99%

Augmented Apriori by Simulating Map-Reduce

Akila¹,

Mani²

2017

IJMSC

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Association rule mining is a data mining technique which is used to identify decision-making patterns by analyzing datasets. Many association rule mining techniques exist to find various relationships among itemsets. The techniques proposed in the literature are processed using non-distributed platform in which the entire dataset is sustained till all transactions are processed and the transactions are scanned sequentially. They require more space and are time consuming techniques when large amounts of data are considered. An efficient technique is needed to find association rules from big data set to minimize the space as well as time. Thus, this paper aims to enhance the efficiency of association rule mining of big transaction database both in terms of memory and speed by processing the big transaction database as distributed file system in MapReduce framework. The proposed method organizes the transactions into clusters and the clusters are distributed among many parallel processors in a distributed platform. This distribution makes the clusters to be processed simultaneously to find itemsets which enhances the performance both in memory and speed. Then, frequent itemsets are discovered using minimum support threshold. Associations are generated from frequent itemsets and finally interesting rules are found using minimum confidence threshold. The efficiency of the proposed method is enhanced in a noticeably higher level both in terms of memory and speed.

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“…Section 6 presents the URL of web application that used for simulating the model. Comparison between the model and KNN imputation [5,15] is shown in section 7. Conclusion is presented at the last section.…”

Section: Introductionmentioning

confidence: 99%

Handling Numerical Missing Values Via Rough Sets

Sallam¹,

Medhat²,

Ghanem³

et al. 2017

IJMSC

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Many existing industrial and research data sets contain missing values. Data sets contain missing values due to various reasons, such as manual data entry procedures, equipment errors, and incorrect measurements. It is usual to find missing data in most of the information sources used. Missing values usually appear as "NULL" values in the database or as empty cells in the spreadsheet table. Multiple ways have been used to deal with the problem of missing data. The proposed model presents rough set theory as a technique to deal with missing data. This model can handle the missing values for condition and decision attributes, the web application was developed to predict these values.

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Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

Cited by 8 publications

References 14 publications

Prediction of Rainfall Trends using Mahalanobis-Taguchi System

Prediction of Rainfall Trends using Mahalanobis-Taguchi System

Augmented Apriori by Simulating Map-Reduce

Handling Numerical Missing Values Via Rough Sets

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