Drought risk assessment and prediction using artificial intelligence over the southern Maharashtra state of India
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ORIGINAL ARTICLE
Drought risk assessment and prediction using artificial intelligence over the southern Maharashtra state of India T. P. Singh1 · Pooja Nandimath1 · Vidya Kumbhar1 · Sandipan Das1 · Prathamesh Barne1 Received: 28 May 2020 / Accepted: 20 August 2020 © Springer Nature Switzerland AG 2020
Abstract Drought is a multidisciplinary concept that can have major impacts on ecological, agricultural, and socio-economic spheres. The accurate monitoring and information of spatio-temporal distribution of agricultural drought are effective means of reducing the farmers’ losses. The space-based technology is proven to provide the detail of land information, which helps to quantify different natural resources and even severity of climate extremes. The biophysical parameters such as precipitation, temperature, soil, and vegetation significantly influence drought monitoring in the regions. An artificial Neural Network (ANN) model based on the integration of Precipitation Condition Index (PCI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Soil Condition Index (SCI) derived from multi-source remote sensing satellite imagery was applied for assessment and prediction of drought. The study was implemented in Sangli, Maharashtra, India from the period 2003–2016 to assess the efficiency of the ANN model for near real-time drought monitoring. In our research, the investigation results for 2006, 2011, and 2016 (a typical dry year) were accurately explored using the ANN model. The multiyear ANN model results were also checked with the actual drought intensity of government records and observation. The proposed system demonstrated 92% accuracy and matched the trend with the ground base stations, hence the ANN model demonstrates the potential of using as drought monitoring indicator capturing both meteorological and agricultural drought information. The output of the study can be utilized for contingency planning by state and local governments. Keywords Drought early warning · MODIS · TRMM · Artificial neural network · Remote sensing
Introduction Drought is a multidisciplinary concept that can be subdivided into a meteorological, agricultural, socio-economical, and hydrological drought (Mishra and Desai 2006; Kumar et al. 2013; Zhang et al. 2013; Russo et al. 2015; Mishra et al. 2018; Tesfaye et al. 2019; Gyamfi et al. 2019; Sobhani and Zengir 2020). It starts with a rainfall deficit, which leads to the stress on soil moisture contents, high land surface temperature and thus affecting vegetation growth. To understand the drought and for its monitoring and prediction; the aspects of precipitation, soil, and vegetation and their interlinking parameters are required for in-depth study (Du et al. 2013; Sharma et al. 2016; Kiem et al. 2016). There is a lot * T. P. Singh [email protected] 1
Symbiosis Institute of Geo‑Informatics, Symbiosis International (Deemed University), Pune, Maharashtra 411016, India
of research work done by different scientists and researchers to study the Indian drought
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