Analysis of Data Mining Techniques for Weather Prediction

Sheikh, Fahad N; Karthick, S.; Malathi, D.; Sudarsan, J. S.; Arun, Chinmayi

doi:10.17485/ijst/2016/v9i38/101962

Cited by 30 publications

(12 citation statements)

References 5 publications

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“…In the study conducted by Mangani and Kousalya (2019), the decision tree method was found to be an effective method in the prediction of crops insurance. In the study by Sheikh et al (2016), the performance of the J48 algorithm (88.2%) was found to be better than that of the Naive Bayes algorithm (54.8%) in the prediction of weather.…”

Section: Figure 1 Decision Treementioning

confidence: 92%

Determination of Factors Affecting Hazelnut Farmers' Agricultural Insurance by Data Mining Algorithms

Çukur¹

2021

alinteri

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Agriculture is an open factory. With this characteristic of it, it faces many risks and uncertainties at any time. Farms are faced with natural, social and economical risks during the agricultural production period. Risk arising from natural conditions is the one of the most important risks in the agricultural sector. Agriculture is the only sector that meets the nutritional needs of the society. So it is very important protection of agriculture against risks. Agricultural insurance is one of the primary measures taken by farmers against these risks and uncertainties. For this reason, it is of great importance for farmers to have agricultural insurance in order to ensure economic sustainability. The main material of the study is the data collected from 70 different hazelnut producers in 7 villages in the Karasu district in the city of Sakarya in Turkey and they were selected with the purposeful sampling method. In the current study, it is aimed to determine the factors affecting farmers' getting agricultural insurance. According to the decision tree model created in the current study, it was determined that the factors affecting farmers' getting insurance are the amount of hazelnut production, non-agricultural income status, farmer's agricultural experience, total agricultural land assets and the profitability of hazelnut production. In the current study, the performances of different classification algorithms were also compared. According to the results of the research, it was determined that the classification algorithms used gave successful results. According to the results obtained with percentage split method, J48 and PART algorithms were determined to have the highest degree of accuracy in the cross validation method.

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Section: Figure 1 Decision Treementioning

confidence: 92%

Determination of Factors Affecting Hazelnut Farmers' Agricultural Insurance by Data Mining Algorithms

Çukur¹

2021

alinteri

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“…On the other hand, Sanjay D. Sawaitul, Prof. K. P. Wagh, Dr. P. N. Chatur [2] discussed different models which were used in the past for weather forecasting. Fahad Sheikh, S. Karthick, D. Malathi, J. S. Sudarsan, and C. Arun [3] has discussed C4.5 and Na¨ıve Bayes algorithm, a novel strategy for foreseeing the kinds of weather dependent on the PV power data and partial meteorological data was discussed by Wenying Zhang, Huaguang Zhang, Fellow, IEEE, Jinhai Liu, Kai Li, Dongsheng Yang, and Hui Tian [4]. G.Vamsi Krishna [5] using the autoregressive integrated moving average (ARIMA) model to estimate the future worth.…”

Section: Related Workmentioning

confidence: 99%

A Survey of Weather Forecasting based on Machine Learning and Deep Learning Techniques

2021

IJETER

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Forecast of future weather is one of the dominant concerns in today’s era. Several techniques that have been used to forecast future weather are statistical analysis, machine learning, and deep learning techniques. Forecast of weather can assist in the decision-making process for the prevention of disasters. While forecasting weekly weather can help people, decide and manage their activities and recreational programs it can also help Smart management programs. The accuracy of the Short term ambient forecast conditions is particularly conducive to the development of forecast management strategies. This paper presents an inclusive survey on such methods based on Machine Learning, and Deep Learning Techniques used to predict weather and discusses the applications, types, and problems of the forecasting.

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“…There are three distinct algorithms for categorical target variables in the DT model, i.e Entropy Reduction, Gini, and Chi-square tests. Previously, research on weather forecasting and climate change found that models produced using DT have small errors compared to other techniques in predicting data mining with large historical data [15], [16].…”

Section: Decision Treementioning

confidence: 99%

Rain Prediction Using Rule-Based Machine Learning Approach

Anwar

Nugrohadi

Tantriyati

et al. 2020

ASSET

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Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.

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Analysis of Data Mining Techniques for Weather Prediction

Cited by 30 publications

References 5 publications

Determination of Factors Affecting Hazelnut Farmers' Agricultural Insurance by Data Mining Algorithms

Determination of Factors Affecting Hazelnut Farmers' Agricultural Insurance by Data Mining Algorithms

A Survey of Weather Forecasting based on Machine Learning and Deep Learning Techniques

Rain Prediction Using Rule-Based Machine Learning Approach

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