1398 1. Maintenance and Adaptation: NCL may need to increase investment in infrastructure resilience to deal with more extreme weather events. This could involve strengthening mine drainage, gabions/concrete walls at edge of slope toe, approach/haul roads, and other facilities to withstand heavy rain or flooding. 2. A Two-Pronged Approach: Preparedness & Early Warning: A two-pronged approach is essential to address this challenge effectively: first, strengthening monsoon preparedness measures, and second, enhancing early warning systems for extreme weather events. NCL undertakes highly meticulous monsoon preparation; however, factors such as cloudbursts and extreme rainfall occurring within a single day or over several consecutive days must be incorporated into volumetric calculations for catchment areas, water routing within and outside mines, and sump and dewatering capacities. A scientifically robust factor of safety has to be mathematically derived by correlating key parameters such as rainfall intensity, catchment response, runoff coefficients, and storage dynamics. This factor will account for uncertainties in climate variability and potential anomalies in storm behavior. By integrating statistical and probabilistic modeling techniques, the derived factor of safety will provide a more resilient framework for infrastructure design, operational planning, and disaster risk mitigation. 3. Technology Integration for Forecasting and Prediction: Use of technology for more localized and accurate prediction of above normal rainfall. • Machine learning approaches are becoming increasingly popular for predicting extreme rainfall. These models can handle large datasets and find complex relationships between various meteorological variables. • Random Forests, Support Vector Machines (SVM), and XGBoost: These algorithms are used to predict the likelihood and intensity of extreme rainfall events. They are effective at handling nonlinear relationships and complex interactions in weather data. • Deep Learning: Neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can be used to detect spatiotemporal patterns in rainfall data. For example, deep learning models like Long Short-Term Memory (LSTM) networks are used for time series forecasting of rainfall. 4. Mission Mausam Government of India, Ministry of Earth Sciences has unveiled Mission Mausam: An initiative to Enhance India’s Weather and Climate Forecasting by 2026. It aims to make India ‘Weather Ready’ and ‘Climate Smart’. The mission seeks to exponentially enhance the country’s weather and climate observations, understanding, modelling and forecasting, leading to better, more useful, accurate and timely services. • Mission Mausam will improve forecasts on both spatial and temporal scales and air quality data and help strategize weather management/intervention in the long run. • The mission will incorporate AI/ML and data-driven models, offering improved forecasts and strengthening community resilience to extreme weather events. NCL has to take the services of such advanced weather forecasting method and take actions as the standard operating procedure developed for this very purpose. By adopting these enhanced forecasting services, NCL can improve its operational readiness and standard operating procedures (SOPs) for extreme weather events.
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