METHODS AND SYSTEMS FOR OPTIMIZING HIDDEN MARKOV MODEL BASED LAND CHANGE PREDICTION

摘要

The present disclosure provides a method and a system for optimizing Hidden Markov Model based land change prediction. Firstly, remotely sensed data is pre-processed and classified into a plurality of land use land cover classes (LULC). Then socio-economic driver variables data for a pre-defined interval of time are provided from a database. A Hidden Markov Model (HMM) is defined with LULC as hidden states and socio-economic driver variables data as observations and trained for generating a HMM state transition probability matrix. Again the defined HMM is trained by taking input data from scenario based temporal variables to generate another set of HMM state transition probability matrix. The generated HMM state transition probability matrix is then integrated with a spatio-temporal model to obtain an integrated model for predicting LULC changes to generate at least one prediction image.