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Forest fires pose significant challenges by disrupting ecological balance, impacting socio-economic harmony, and raising global concerns. North-East India (NEI) experiences high incidences of forest fires, making it crucial to implement suitable management measures considering the driving forces influencing fire likelihood. This study aims to identify forest fire susceptibility zones in NEI by using five machine-learning modeling approaches, Boosted Regression Tree (BRT), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Tree (CART), and Multivariate Adaptive Regression Splines (MARS), and an ensemble method. Forest fire data from the SNPP – VIIRS sensor (2018–2019) were rectified for spatial autocorrelation. Thirty-two responsive predictor variables related to topographic, climatic, biophysical, and anthropogenic factors were used as model inputs and multicollinearity analysis was performed to eliminate highly correlated predictors. Results indicate that the southern and southeastern regions of NEI, characterized by ample solar radiation, enhanced vegetation index, high human population density, and jhum cultivation, contribute significantly to higher susceptibility to forest fires. The Random Forest model performs best among the models employed, achieving an AUC value of 0.87. The ensemble susceptibility map, binarized based on AUC weighting, covers 29.54% of the total geographic area and 44.42% of the forested area of NEI. The vulnerability levels vary among states, with Mizoram showing the highest susceptibility at 89.27% and Sikkim exhibiting the lowest vulnerability at only 0.49% of their respective geographic areas. This map provides valuable insights for implementing effective forest fire management plans in the region. Moreover, the methodology utilized in this study, which incorporates satellite imagery, GIS techniques, and improved modeling techniques, can be replicated in any geographical region worldwide to facilitate effective forest fire management at a regional to large scale. [Display omitted] • Forest fire zonation via BTR, RF, SVM, CART, and MARS. RF performed best. • Ensemble map covers 29.54% total, 44.42% of forests susceptible to forest fire. • Study aids regional fire strategies, with replicability globally. [ABSTRACT FROM AUTHOR]

Political development is not an end-product but a continuing process- 'a persistent capacity for coping with a permanent revolution'. It is a process by which a political system acquires an increased capacity to sustain successfully and continuously new types of goals, demands and the creations of new types of organisations'. It involves the creations of political institutions sufficiently adaptable, complex, autonomous and coherent to absorb and order the participation of these new groups and to promote social and economic change in the society. Political development in Mizoram has a long and eventful history starting from the Pre-British period till the signing of the Mizoram Peace Accord and the establishment of Mizoram state. The present paper tries to locate the stages of political development in Mizoram. [ABSTRACT FROM AUTHOR]