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Wednesday, June 29, 2016

Sunday, November 22, 2015

Study of Effect of Climatological Variables on Crop Yeild Estimation Using Multiple Linear Regression #IJSRD

Study of Effect of Climatological Variables on Crop Yeild Estimation Using Multiple Linear Regression


Author(s):  Dr. T. M. V. Suryanarayana , WREMI, The M.S. University of Baroda

Keywords: Climatological Data, Crop Yield, Multiple Linear Regression, R.M.S.E., Coefficient of Correlation

Abstract

An attempt has been made to carry out the study of determining the predominant climatological variables in estimating the crop yield. The climatological data are collected for the period 1981- 2006 and correlated with yield of cotton in Vallabh Vidyanagar using Multiple Linear Regression. The Climatological variables considered are Maximum Temperature, Minimum Temperature, Relative Humidity, Wind Speed and Sunshine Hours. The multiple linear models have been developed, to study their impact in prediction of the crop yield. The study has been carried out with eight different combinations of the five independent variables considered, to correlate with the crop yield. In each combination, i.e 1 to 8, the whole data is divided into proportions for training and Validation, such as 70% and 30% & 60% and 40% respectively. The developed Multiple Linear Regression Models are evaluated based on the performance indices such as Root Mean Squared Error and Correlation Coefficient. Based on the evaluation, the models developed are found to perform better in 60%-40% proportion of the data considered for the Study. Therefore in this considered proportion of the dataset, the models developed are ranked based on the obtained R.M.S.E. and R. The results clearly show that the consideration of all the variables, yield the best model with minimum R.M.S.E. and maximum R, followed by the combinations considering Maximum Temperature, Minimum Temperature, Relative Humidity as dependent variables along with/without Wind Speed/Sunshine hours. Moreover excluding the Relative Humidity, and trying the combinations of Maximum Temperature, Minimum Temperature along with/without Wind Speed/Sunshine Hours yields the poor models with maximum R.M.S.E. amd Minimum R. Hence considering multiple linear regression models and the eight combinations studied, it reveals that the yield of a crop is very much dependent on maximum and minimum temperatures & relative humidity.

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Saturday, September 5, 2015

IJSRD LEADING E-JOURNAL CALL FOR PAPER IN DATA MINING

Dear Researchers/Authors,

IJSRD is promoting a new field of this Digital Generation-“Data Mining”. 

In accordance to it IJSRD is inviting research Papers from you on subject of Data Mining. This is under special Issue Publication by IJSRD. In addition to this authors will have a chance to win the Best Paper Award under this category.

To submit your research paper on Data Mining Click here


 IJSRD

What is Data Mining..?

Data mining (the analysis step of the "Knowledge Discovery in Databases" process. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records, unusual records and dependencies.The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

(1) Selection
(2) Pre-processing
(3) Transformation
(4) Data Mining
(5) Interpretation/Evaluation.

To know more…….

Data mining involves six common classes of tasks:

Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.

Association rule learning (Dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

Regression – attempts to find a function which models the data with the least error.

Summarization – providing a more compact representation of the data set, including visualization and report generation.

Application Areas….


Games

            They are used to store human strategies into databases and based on that new tactics are designed by Computer ( in association with Machine Learning, Artificial Intelligence)

Business

            Businesses employing data mining may see a return on investment. In situations where a large number of models need to be maintained, some businesses turn to more automated data mining methodologies.In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business information. Examples of what businesses use data mining for include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-selling to existing customers, and profiling customers with more accuracy.

Science and engineering

            In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.

Human rights

            Data mining of government records – especially records of the justice system (i.e., courts, prisons) – empowers the revelation of systemic human rights infringement in association with era and publication of invalid or deceitful lawful records by different government organizations

Medical data mining

            Some machine learning algorithms can be applied in medical field as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in databases.

Spatial data mining

            Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Data mining offers great potential benefits for GIS-based applied decision-making.

Temporal data mining

            Data may contain attributes generated and recorded at different times. In this case finding meaningful relationships in the data may require considering the temporal order of the attributes.

Sensor data mining

            By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.

Visual data mining

            During the time spent transforming from analogical into computerized, vast datasets have been created, gathered, and stored finding measurable patterns, trends and information which is covered up in real data, with a specific end goal to manufacture prescient formations(patterns).