Cloud-Fog based framework for drought prediction and forecasting using artificial neural network and genetic algorithm

ABSTRACT

Drought is one of the most recurrent natural disasters with cataclysmic effects on water budget, crop production, economic progression and public health. These consequences are magnified by the climate change leading to more intense drought conditions. A number of drought indices have been presented to calibrate the drought severity with its own strengths and limitations. Many of them are region-specific and unable to exhibit the alterations in significant drought inducing elements. Internet of Things (IoT) is well-suited for continuous monitoring, collection and analysis of different environmental phenomena. The dimensionality of the data collected about drought inducing attributes temperature, humidity, precipitation, evapotranspiration, groundwater, soil moisture at different depths, streamflow and season is reduced using PCA (Principal Component Analysis) at fog layer. Cloud layer estimates the drought severity level using Artificial Neural Network (ANN) whose parameters are optimised with Genetic Algorithm (GA) to get more accurate system and ARIMA method is used to forecast the drought for different time frames. Experimentation done on data collected from government websites shows that proposed system performs well in terms of accuracy, sensitivity, specificity, precision and F-measure with values 95.03%, 90.6%, 96.73%, 91.42% and 91.01%.

KEYWORDS:

• fog computing
• cloud computing
• ANN-GA (Artificial Neural Network-Genetic Algorithm)
• Principal Component Analysis (PCA)
• ARIMA model

Disclosure statement

No potential conflict of interest was reported by the authors.