सीएसआईआर-राष्ट्रीय डेटा विज्ञान एवं कृत्रिम बुद्धिमत्ता संस्थान

The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

by Nikhila Yaladanda, Rajasekhar Mopuri, Hariprasad Vavilala, Kantha Rao Bhimala, Krushna Chandra Gouda, Madhusudhan Rao Kadiri, Suryanarayana Murty Upadhyayula & Srinivasa Rao Mutheneni

Abstract

3-D Crustal structure in Kumaon–Garhwal Himalaya using joint inversion of receiver functions and surface wave group velocity

by Ashish, Gokul Saha and Shyam S Rai

Summary

We investigate the 3-D shear velocity (Vs) structure of the crust beneath the Kumaon Garhwal Himalaya using joint inversion of interpolated receiver functions from 57 seismic stations, and Rayleigh wave group velocity dispersion data in the period 2 to 100 s with significantly improved horizontal resolution of about 25 km. The velocity image reveals several important features. In the shallow crust, the Main Himalayan Thrust (MHT) is characterised as a flat-ramp-flat structure, inferred from the presence of low Vs of 3.1–3.4 km/s representing wet sediments dragged along the MHT and lying above the crystalline Indian crust of Vs ∼ 3.6 km/s. The MHT is at a depth of about 8 km beneath the southern edge of the Himalaya, dipping at 3^○ to the north. At the front of the High Himalaya, the dip increases significantly to about 35^○–40^○ representing the ramp and reaching a depth of 24 km. Farther north beneath the High Himalaya, the MHT continues as a nearly flat structure. The middle crust (20–30 km) has reduced Vs (3.3–3.5 km/s) below the northern part of the Lesser Himalaya, possibly due to the presence of fluid released by metamorphism of the subducting Indian crust along with the presence of mica produced as a consequence of deformation. The thickness of the crust is ∼50 km beneath the sub and Lesser Himalaya and increases abruptly in the front of the High Himalaya to 60 km and remains so till the southern part of Tethys Himalaya. The observed thick crust with lower seismic velocity (and rigidity) beneath the High Himalaya could be responsible for its high topography. We report almost 6–8 km thinning of the crust in the eastern segment of Garhwal Himalaya adjoining Nepal.

Source: https://doi.org/10.1093/gji/ggad044 

Trends of seasonal and annual rainfall of semi-arid districts of Karnataka, India: application of innovative trend analysis approach

by KK Chowdari, Surajit Deb Barma, Nagaraj Bhat, R Girisha, K.C. Gouda & Amai Mahesha

Abstract: 

Trend analysis of rainfall is often carried out in water resources management to understand its distribution over a given region. The cumulative seasonal and annual rainfall derived from monthly datasets spanning 102 years (1901–2002) for 11 districts of the semi-arid Karnataka, India, was used for the trend analysis. The two-step homogeneous test approach was carried out on all the time series. Then, lag-1 autocorrelation was conducted only on homogeneous time series. Only 78.18 % of the total time series data were detected as homogeneous, and 95.35% of time series data were found to have insignificant autocorrelation. Then, the Innovative Trend Analysis (ITA) method was applied to 43 homogeneous rainfall time series, as well as to 41 time series using the MK and SR tests, and to two time series using the mMK test. The MK and SR tests detected a significant trend in 14.63% of the time series, while the ITA method was able to detect a trend in 93.02% of the total time series data. The MK and SR tests revealed significant trends in winter and post-monsoon season precipitation for two districts, but only for one district in the case of summer and annual rainfall. No trend was identified for monsoon season precipitation. The mMK test showed a positive trend for the post-monsoon season in a district, while the ITA method revealed significant trends for all seasons in most districts. The sub-trend analysis revealed trends that traditional methods were unable to detect.

Deep learning methods for scientific and industrial research

by G.K. Patra, Kantha Rao Bhimala, Ashapurna Marndi, Saikat Chowdhury, Jarjish Rahaman, Sutanu Nandi, Ram Rup Sarkar, K.C. Gouda, K.V. Ramesh, Rajesh P. Barnwal, Siddhartha Raj, Anil Saini

Abstract
Deep learning (DL) is a very powerful computational tool for various applications in scientific and industrial research which can be real-time implemented for societal benefits. Several factors impact the development of optimized DL models for better prediction including the amount of quality sample data, domain-specific knowledge, and the architecture of the model for extraction of the useful features/patterns from the data. The present chapter demonstrates the state-of-the-art DL methodologies used by the researchers from different laboratories under the Council of Scientific and Industrial Research (CSIR), India to solve important research activities across several sectors like Medical, Healthcare, Agriculture, Energy, etc. The Convolutional Neural Network (CNN) techniques are utilized for Tumor diagnosis, classifying molecular subtypes of glioma tissues, and predicting driver gene mutations in glioma. Similarly, the Long short-term memory (LSTM) model is applied for the assessment of crop production, and transfer learning is used for the classification of tea leaves. Further, the ensemble LSTM methodology is implemented for short-term prediction of wind speed to enhance the renewable energy sectors. Finally, the multivariate LSTM models were developed by integrating the weather parameters for the prediction of covid-19 spread over different states in India which is an input for policy planning and supply chain management during the pandemic time. All the use cases are being validated and the results are quite satisfying and provide confidence for the real-time application of DL for scientific and industrial research and societal benefit to the common people.

Source: https://doi.org/10.1016/bs.host.2022.12.002