Development of Cloud based Model for drought monitoring System

Abstract

Drought monitoring plays a crucial role in managing water resources and planning agricultural activities effectively. However, traditional methods relying on ground-based observations have limitations in terms of spatial coverage and temporal resolution. To overcome these challenges, a cloud-based model is proposed in this study, which combines the Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Seasonal Autoregressive Integrated Moving Average (SARIMA) model. newline newlineThe proposed system leverages remote sensing data obtained from satellite imagery along with meteorological data collected from weather stations. The SPI serves as an indicator of drought severity by measuring the deviation of precipitation from its long-term average. Complementing the SPI, the NDVI and NDWI derived from satellite imagery provide information on vegetation health and water availability respectively. By integrating these indices, the accuracy of drought monitoring is enhanced. newline newlineThe cloud-based model takes advantage of the storage and computing capabilities offered by cloud infrastructure, allowing efficient handling of large volumes of data and complex calculations. The SPI, NDVI, and NDWI data are preprocessed and integrated within the cloud-based system. The SARIMA model, known for its effectiveness in time series forecasting, is applied to this integrated dataset to predict future drought conditions. The model considers temporal patterns and seasonality, enabling accurate and reliable predictions. newline newlineThe cloud-based system incorporates a user-friendly web interface that enables stakeholders to easily access drought monitoring information. Users can visualize historical and forecasted drought conditions, analyze trends, and make informed decisions regarding water management and agricultural practices. The system also provides alerts and notifications based on predefined drought thresholds, facilitating timely actions to mitigate the impact of drought events.