Time Variant Multi Feature Census Analysis for Efficient Prediction of Migration Risks in Agriculture

Abstract

The changing dynamics of life and other activities has encouraged the human society to move or migrate between regions of any country. The population in different region and state of India gets increasing and it is necessary for the planning commission to monitor the migrations incur in every year. Number of approaches available for the prediction and analysis of migrations to perform several decision making but suffer to achieve higher performance. In this paper, an efficient time variant multi feature census analysis algorithm is presented. The method keeps track of migrations made in every time stamp. The reasons and purpose of migration also being recorded every year like study, business, work, marriage and others. Using these features the method performs analysis on the ratio of migration in different aspects and predicts the ratio of migration. The result of analysis would be used to perform several planning activities by the government. The method introduces higher performance in migration prediction.

Using Time-Series Generative Adversarial Networks to Synthesize Sensing Data for Pest Incidence Forecasting on Sustainable Agriculture

“…For sudden increases in pest populations, immediate control may not be possible. Technological advances have yielded more sophisticated methods for time-variant analysis for data prediction [12]. However, such prediction relies on a large amount of historical data.…”