Software-Defined Flood Alert System Using Rule-Based Threshold Logic for Places like Sirsi which are located in Ecological Sensitive Areas of Western Ghats

Due to physical limitations, uncontrolled urbanization, and climate-driven monsoon instability, areas in the Western Ghats that are ecologically sensitive suffer increasing flood hazards. Conventional flood management approaches exclude vulnerable agricultural areas, rely on expensive infrastructure, and lack hyper local precision. In order to fill these deficiencies, this study uses Google Colab and Python to create and validate an accessible, rule-based flood alarm system. Using open-source libraries (Pandas, Matplotlib) for data processing and visualization, the system generates real-time alerts using dual-threshold logic (rainfall >450 mm and river level >5.0 m). 100% accuracy in identifying flood months (July–August) with no false positives was validated by validation using environmental data from 2024. The technology offers browser-based access for farmers and local authorities, eliminates hardware dependencies, and lowers expenses from ₹15–20 lakhs/sensor to almost free. Important results include a replicable framework for 11 flood-prone talukas in Karnataka, risk probability visualizations, and SMS-alert templates for evacuation. The technology has the ability to cut crop losses by 35% and is in line with national policies (Sendai Framework, NDMA). To improve spatiotemporal precision, future research will incorporate IoT sensors, daily rainfall resolution, and crowd sourced validation.