Modeling and predicting weather in agro-climatic scarcity zone using iterative approach

摘要: Weather predictions could be used to give decision support guidelines for the agricultural management. The yield productivity depends on crop protection and its effective management, which increased by avoiding losses due effect of low high-temperature damage. In Maharashtra state, seasonal changes affect potential susceptible crops livestocks, variations in temperature. Therefore, it is essential decisions are avoid damages from extreme weather conditions. main goal study predictive analytics agro-climatic scarcity region using iterative approach. this study, approach proposed based linear regression polynomial predicting methods. research falls having inadequate rainfall, variation temperature, dry land. So, methods applied designed predict future Both models provide findings that useful farming Also, comparative plots depicted actual estimated maximum minimum values humidity, rainfall results effectiveness performance tested statistical tests error measure statistics. prediction accuracy at 95% confidence level tested. estimates much better compared measures estimators, such as mean, variance, median, standard deviation, kurtosis, mean squared error, root-mean-squared correlation coefficient evaluated discussed detail. Lastly, hypothesis testing ANOVA, t test, f test z predicted data model. frost conditions occurrence probability year 2013 (20% month May 38.33% December–January) noted. demonstrate visualization, accuracy, suitability managing products livestock.