by Neethu C & K V Ramesh
Heat waves (HW) are increasing in intensity, frequency, and duration. The projected changes in the characteristics of HW at the regional level are essential input to develop mitigation strategies to minimize social risks in densely populated regions. In this study, we examine the projected spatiotemporal changes in heat wave characteristics under different climate change scenarios using simulations of Coupled Model Intercomparison Project phase 6 (CMIP6) in seven temperature homogeneous zones of India, i.e., North West (NW), North Central (NC), West Coast (WC), East Coast (EC), Interior Peninsula (IP), Western Himalaya (WH) and North East (NE). The results show that the area of occurrence of a daily maximum temperature above 43∘C is projected to increase about 16-fold over WC, 10-fold over EC, and in other zones in the range of 1-3 fold. The warm days are projected to increase fivefold over WC and threefold over NW, EC, IP, and WH. In India, HW days are projected to increase by 7-8 days in the near future (2025-2050) and by 10-17 days in the far future (2076-2100), while under SSP585 over WH (24 days), NW (19 days), and other zones 12-15 days in the far future. EC and WC are plausible to be more vulnerable under SSP370 and SSP585, with an increase in HW intensity (>1.5∘C). The area of occurrence of long-lasting heat waves over WC is expected to have a drastic increase of more than 20-fold under all scenarios, while increasing 12-fold over IP and 8-fold over NC, EC, and WH under SSP585. The projected HW days will be more intense in the coastal zones and more frequent over WH and NW.
by Ramesh Kalidhasan Vasanthakumari, Rakesh Vasudevan Nair and Venkatesh Gowda Krishnappa
The Convolutional Neural Network (CNN) algorithm is used to classify multispectral images of labelled EuroSAT data from Sentinel-2 satellite. The main objective of this study to examine the role of newly proposed activation function, Modified Rectified Linear Unit (MReLU) in improving multi-spectral image classification accuracy. The experimental design deployed a 5-layer CNN with activation function Rectified Linear Unit (ReLU) and the MReLU having 27 learning rates ranged from 0.0001 to 0.01 with 10 % increment and 8 optimizers. ReLU has a diminishing gradient problem since it does not consider the significant information present in the negative values of the convolution layer for different spectral bands. In order to overcome this short coming, we have designed the MReLU which allows the absolute values of the small negative gradient and thereby introducing higher nonlinearity in the output. The experimental results show that the image classification accuracy of the CNN with ReLU and MReLU activation functions is definitely higher than the pre-trained ResNet-50 model which is a complex and computationally expensive architecture. The MReLU has advantage over ReLU in land use classification, particularly in the Industrial, Herbaceous Vegetation, and Permanent Crop classes where accuracies were typically low. In summary, this paper highlights the importance of deploying a suitable activation function and hyperparameter tuning in the CNN model for improving the accuracy of multispectral image classification.
by V. Kiran Kumar, K. V. Ramesh & V. Rakesh
Advance prediction of crop yield is very critical in the context of ensuring food security as the region specific challenges in social and environmental conditions often infringe plan of policy makers. This study presents a generic methodology to configure and fine tune the state-of-the-art Long Short-Term Memory (LSTM) based Deep Learning (DL) model through hyperparameter optimization for prediction of yield (annual crop production) in Wheat, Groundnut and Barely over India based on multiple independent input variables identified using multicollinearity test. The Monte Carlo cross-validation method is used to validate the optimized LSTM models. Results from the LSTM model tuning showed that among the 4 optimizers tested, Adam was found to perform better irrespective of the crop and Bi-LSTM outperformed sLSTM in terms of prediction accuracy. The percentage reduction in error with Bi-LSTM compared to sLSTM in predicting wheat and groundnut crop yield was 39% and 13% respectively while in case of barley crop, error reduction was marginal (0.34%). The performance of optimized Bi-LSTM model is compared with the performance of traditional machine learning (ML) models such as support vector regression (SVR) and SVR polynomial {2nd and 3rd order}, Auto Regressive Integrated Moving Average (ARIMA) and ARIMAX (ARIMA with exogenous variables) and Vector Auto-regression (VAR). The Bi-LSTM model is found to be superior to ML models; the percentage reduction in mean absolute scaled error with the Bi-LSTM compared to the best performing ML model was 94%, 72%, and 71% in predicting wheat, groundnut and barley yield respectively. This study showed that by choosing proper explanatory (independent) variable and hyperparameter optimization, a simple (single layer) structure of deep neural network (LSTM) outperformed traditional ML models in terms of accuracy for crop yield prediction application.
by P. Ipsita, V. Rakesh & G. N. Mohapatra
The role of ocean–atmosphere interaction in modulating the track and intensity of two cyclonic storms, Nisarga and Nanauk, which originated from almost similar locations in June in the Arabian Sea with completely different tracks, is investigated in this study. Sea surface temperature (SST), steering flow, relative vorticity, latent heat flux (LHF), specific humidity, vertical wind shear (VWS), outgoing longwave radiation (OLR), convective available potential energy (CAPE), and Madden–Julian oscillation (MJO) phases are analyzed to elucidate the causes of contrasting characteristics of these two cyclones. During the progression and intensification of the storm Nanauk, SST decreased drastically (magnitude of ~ 4 °C), while VWS anomaly is found to be increased (~ 8 ms−1), followed by entrainment of dry air leading to a decrease in upward LHF anomaly (~ 3.5 to 4 J m−2) hindering further moisture supply. Moreover, just before the storm initiation, the CAPE anomaly was around − 800 to − 1000 J kg–1 and the MJO condition was also unfavorable for the continuous intensification of the cyclone. All these factors contributed to the quick dissipation of cyclone Nanauk. However, high SST (~ 31 °C) along with other favorable atmospheric conditions contrary to cyclone Nanauk provided a conducive environment for Nisarga to intensify and prevail. A high-pressure anticyclonic circulation at the eastern side of Nisarga over Indian land dragged the storm north-eastwards across the Maharashtra coast. The convective phase of MJO (magnitude > 1) along with the strong lower tropospheric westerly wind at the west side of the convective system also helped cyclone Nisarga to propagate eastward.
by S Vishal Gupta, Imtiyaz A Parvez, and Prosanta K Khan
Deep sedimentary valleys entrap and amplify seismic waves, which is further responsible for site-specific amplification over the allied region. Kashmir valley is a deep and active sedimentary basin in the northwest portion of the Himalayan lap. To characterize the site response for the intermountain Kashmir valley for the first time, we acquire single station microtremor measurements using Lennartz (three components/5s seismometer) at 141 sites with a grid of (5 km × 5 km) and two-dimensional (2D) array measurements using seven units of recording station of different geometry pattern at 38 sites across different lithological setup within the basin. We present here (1) a first-order predominant fundamental frequency (0.21–10.19 Hz) and subsequent sedimentary thickness maps for the entire valley, reflecting deep deposition with spatial heterogeneity in the area using the horizontal-to-vertical spectral ratio (HVSR) technique; (2) spatial classification of generated HVSR curves linking subsurface geomorphology and post strong ground-motion scenario in the valley; (3) extraction of the best dispersion characteristics of Rayleigh waves (fundamental mode) from all recorded vertical signals at each array site using frequency–wavenumber (F-K) method with an emphasis on signal synchronicity; and (4) retrieval of one-dimensional (1D) shear wave velocity (VS) profiles across the valley from the inversion of dispersion curves using the neighborhood algorithm. Distinct model parameterizations were tested for the inversion to achieve the optimal inversion misfit. The collocated 1D VS profiles are consistent with newly drilled borehole logs information. Besides, the presented first-ever ambient noise survey–based site response study meets the objectives of site-specific seismic hazard and risk analysis of the Kashmir valley at the regional scale.
- Analysis of heat wave over different physiographical regions in India
- Dynamics of Indian summer monsoon in different phases
- Thermodynamic structure of the atmospheric boundary layer over a coastal station in India for contrasting sky conditions during different seasons
- Dynamical influence of MJO phases on the onset of Indian monsoon