Artificial Intelligence-Based Model For Drought Prediction and Forecasting

Drought is considered as one of the most extremely destructive natural disasters with catastrophic impact on hydrological balance, agriculture outcome, wildlife habitat and financial budget. Therefore, there is a need for an efficient system to predict and forecast drought situations. There are a number of drought indices to assess the severity of droughts considering different causing factors. Most of them does not take important factors into consideration. Internet of Things (IoT) has demonstrated phenomenal growth and has successfully worked in monitoring environmental conditions. This paper proposes an IoT-enabled fog-based framework for the prediction and forecasting of droughts. At the fog layer, the dimensions of the data are decreased using singular vector decomposition. Artificial neural network with genetic algorithm classifier is used to assess drought severity category to the given event and Holt-Winters method is used to predict the future drought conditions. The proposed system is implemented using datasets from government agencies and it proves its effectiveness in assessing drought severity level.