A New Agricultural Drought Index to Characterize Agricultural Drought Using Data Mining Techniques

Drought monitoring is a critical task as its occurrence and extent vary according to many factors like drought type, risk, agricultural losses, and impact. Monitoring drought is important because the footprint of this hazard is larger than that of other natural hazards. Many drought indices are developed to monitor complex drought conditions. The intensity and severity of drought in a particular region and at a particular time can be tracked by the drought indicator. In this research, a new agricultural drought index, Yield-Evapotranspiration Drought Index (YEDI) is developed using crop yield, potential, and reference crop evapotranspiration. Data mining and Neural Network techniques have been used to model the drought index. The agricultural and climatic data used is selected from the year 1983 to 2015 (33 years) from the period of June to October (Kharif period) for Maharashtra state in India. The drought index generates the positive values which are further divided into a range of high, medium, and low intensities of drought. SPI and SPEI indices are used for validation against YEDI. Results show that there is a correlation between YEDI and SPEI whereas a low correlation is between YEDI and SPI. YEDI proves to be useful for agricultural drought monitoring.