Association Rule Mining and Classifier Approach for 48-Hour Rainfall Prediction Over Cuddalore Station of East Coast of India

Meganathan, S.; Sivaramakrishnan, T. R.

doi:10.19026/rjaset.5.4510

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Weather day or temperature patterns [4,5] are used for accurate prediction of rainfall that can be used for cultivation of crops. Classification [6,7] algorithms like Bayesian and Naïve Bayesian classifiers were used for predicting rainfall for a region.…”

Section: Introductionmentioning

confidence: 99%

Association Rule Mining for South West Monsoon Rainfall Prediction and Estimation over Mumbai Station

Varahasamy*¹,

Meganathan²,

Karthik³

2019

IJRTE

Self Cite

View full text Add to dashboard Cite

Rainfall is important for agricultural yield and hence early prediction is required. It has a vital role in the improving the economy of a country. Accurate and timely weather prediction for rainfall forecasting has been one of the most challenging problems around the world as it changes the physical characteristics of the hydrologic system. Rainfall prediction model involves observation of weather data, deriving knowledge from it and implementing using computer models. The proposed work observed rainfall during south-west monsoon months of Mumbai (Latitude 19.0760°N / Longitude 72.8777°E) city. Predictive Apriori Algorithm was used to derive association rules for spot prediction, 24 hours ahead prediction and 48 hours ahead prediction, also to estimate a no rain day, moderate rain day and heavy rain day.

show abstract

Section: Introductionmentioning

confidence: 99%

Association Rule Mining for South West Monsoon Rainfall Prediction and Estimation over Mumbai Station

Varahasamy*¹,

Meganathan²,

Karthik³

2019

IJRTE

Self Cite

View full text Add to dashboard Cite

show abstract

Data mining as a tool for hot day prediction during summer monsoon

Prashanthi

Meganathan

Krishnan

et al. 2016

2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT)

View full text Add to dashboard Cite

A New Associative Classification Algorithm for Predicting Groundwater Locations

Aburub

Hadi

2018

J. Info. Know. Mgmt.

View full text Add to dashboard Cite

In this paper, we study the problem of predicting new locations of groundwater in Jordan through the application of a proposed new method, Groundwater Prediction using Associative Classification (GwPAC). We identify features that differentiate locations of groundwater wells according to whether or not they contain water. In addition, we survey intelligent-based methods related to groundwater exploration and management. Three experimental analyses were conducted with the objective to evaluate the capability of data mining algorithms using real groundwater data from the Ministry of Water and Irrigation. In the first experiment, we investigated the performance of GwPAC against three well-known associative classification algorithms, namely CBA, CMAR and FACA. Furthermore, three rule-based algorithms — C4.5, Random Forest and PBC4cip — were investigated in the second experiment; further, so as to generalise the capability of using data mining for solving the groundwater detection problem, four benchmark algorithms — SVMs, NB, KNN and ANNs — were evaluated in the third experiment. From all the experiments, the results indicated that all considered data mining algorithms predict locations of groundwater with acceptable classification rate (all classification accuracies [Formula: see text]%), and can be useful methods when seeking to address the problem of exploring new groundwater locations.

show abstract

Association Rule Mining and Classifier Approach for 48-Hour Rainfall Prediction Over Cuddalore Station of East Coast of India

Cited by 3 publications

References 11 publications

Association Rule Mining for South West Monsoon Rainfall Prediction and Estimation over Mumbai Station

Association Rule Mining for South West Monsoon Rainfall Prediction and Estimation over Mumbai Station

Data mining as a tool for hot day prediction during summer monsoon

A New Associative Classification Algorithm for Predicting Groundwater Locations

Contact Info

Product

Resources

About