Forest fire susceptibility mapping using multi-criteria decision making and machine learning models in the Western Ghats of India.

Forest fires have significantly increased over the last decade due to shifts in rainfall patterns, warmer summers, and long spells of dry weather events in the coastal regions. Assessment of susceptibility to forest fires has become an important management tool for damage control before the occurrence of fires, which often spread very rapidly. In this context, the current study was undertaken with the aim to map forest areas susceptible to fire in the state of Goa (India) using remote sensing (RS) and geographic information system () derived variables through an analytical hierarchy process (AHP) and machine learning techniques namely random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB).

Assessment of future water demand and supply using WEAP model in Dhasan River Basin, Madhya Pradesh, India.

Understanding the available resources and the needs of those who use them is necessary for the evaluation and allocation of water resources. The main sectors utilizing the basin water resources are agriculture, drinking water, animal husbandry, and industries, and the efficient and rational management of water resources to be distributed among those different sectors of activity is vital. This study attempts to develop an integrated water resource management system for the Dhasan River Basin (DRB) by employing a scenario analysis approach in conjunction with Water Evaluation and Planning Model (WEAP) to analyze trends in water use and anticipated demand between 2015 and 2050, simulating five possible scenarios (I, II, III, IV, and V) as for external driving factors.