V. Raju

Wildfires present a significant danger to both the environment and the economy. Advanced disaster management necessitates powerful prediction models. In this study, we propose a Context-Driven Hybrid Wildfire Forecasting System that utilizes ensemble learning to increase the accuracy of predictions. In order to create a high-precision wildfire forecast, we combine XGBoost, Long Short-Term Memory (LSTM) networks, and Context-Based Learning Approach. Context based learning model methodology creates a Risk Score by analyzing the past weather and wildfire data and then detecting the abnormalities using a distance measurement and clustering approach. LSTM and XGBoost models incorporate this risk score as an input feature together with the historical datasets to capture the complex patterns of wildfires and temporal relationships. To make the predictions of the previous models even more precise, we employ meta-learning with Linear Regression, thereby creating a more reliable and accurate wildfire prediction system.

To shape India's inclusive and sustainable development, the government has made social infrastructure such as education, health and social security a top precedence. The government take steps to recover the efficiency of expenditure through the combination of projects and improves expenditure on human capital. Various labor reform method are being implemented, including the legislature, to create jobs and give sustainable livelihoods to people heavily involved in the casual economy. Closing gender gaps in education, skills development, employment and income and sinking social inequalities in society are fundamental objectives of the development policy to improve human skills.