Grey wolf optimizer: a new strategy to invert geophysical data sets

There is no meta‐heuristic approach best suited for solving all optimization problems making this field of study highly active. This results in enhancing current approaches and proposing new meta‐heuristic algorithms. Out of all meta‐heuristic algorithms, swarm intelligence is preferred as it can preserve information about the search space over the course of iterations and usually has fewer tuning parameters. Grey Wolves, considered as apex predators, motivated us to simulate Grey Wolves in the optimization of geophysical data sets. The grey wolf optimizer is a swarm‐based meta‐heuristic algorithm, inspired by mimicking the social leadership hierarchy and hunting behaviour of Grey Wolves. The leadership hierarchy is simulated by alpha, beta, delta and omega types of wolves. The three main phases of hunting, that is searching, encircling and attacking prey, is implemented to perform the optimization. To evaluate the efficacy of the grey wolf optimizer, we performed inversion on the total gradient of magnetic, gravity and self‐potential anomalies. The results have been compared with the particle swarm optimization technique. Global minimum for all the examples from grey wolf optimizer was obtained with seven wolves in a pack and 2000 iterations. Inversion was initially performed on thin dykes for noise‐free and noise‐corrupted (up to 20% random noise) synthetic data sets. The inversion on a single thin dyke was performed with a different search space. The results demonstrate that, compared with particle swarm optimization, the grey wolf optimizer is less sensitive to search space variations. Inversion of noise‐corrupted data shows that grey wolf optimizer has a better capability in handling noisy data as compared to particle swarm optimization. Practical applicability of the grey wolf optimizer has been demonstrated by adopting four profiles (i.e. surface magnetic, airborne magnetic, gravity and self‐potential) from the published literature. The grey wolf optimizer results show better data fit than the particle swarm optimizer results and match well with borehole data.     

Probabilistic Assessment of Earthquake Recurrence in Northeast India and Adjoining Regions

Northeast India and adjoining regions (20°–32° N and 87°–100° E) are highly vulnerable to earthquake hazard in the Indian sub-continent, which fall under seismic zones V, IV and III in the seismic zoning map of India with magnitudes M exceeding 8, 7 and 6, respectively. It has experienced two devastating earthquakes, namely, the Shillong Plateau earthquake of June 12, 1897 (M _w 8.1) and the Assam earthquake of August 15, 1950 (M _w 8.5) that caused huge loss of lives and property in the Indian sub-continent. In the present study, the probabilities of the occurrences of earthquakes with magnitude M ≥ 7.0 during a specified interval of time has been estimated on the basis of three probabilistic models, namely, Weibull, Gamma and Lognormal, with the help of the earthquake catalogue spanning the period 1846 to 1995. The method of maximum likelihood has been used to estimate the earthquake hazard parameters. The logarithmic probability of likelihood function (ln L) is estimated and used to compare the suitability of models and it was found that the Gamma model fits best with the actual data. The sample mean interval of occurrence of such earthquakes is estimated as 7.82 years in the northeast India region and the expected mean values for Weibull, Gamma and Lognormal distributions are estimated as 7.837, 7.820 and 8.269 years, respectively. The estimated cumulative probability for an earthquake M ≥ 7.0 reaches 0.8 after about 15–16 (2010–2011) years and 0.9 after about 18–20 (2013–2015) years from the occurrence of the last earthquake (1995) in the region. The estimated conditional probability also reaches 0.8 to 0.9 after about 13–17 (2008–2012) years in the considered region for an earthquake M ≥ 7.0 when the elapsed time is zero years. However, the conditional probability reaches 0.8 to 0.9 after about 9–13 (2018–2022) years for earthquake M ≥ 7.0 when the elapsed time is 14 years (i.e. 2009).     

Accumulation of Heavy Metals by Chickpea Grown in Fly Ash Treated Soil: Effect on Antioxidants

Chickpea grown in fly ash (FA) treated soil (25, 50, and 100% FA) was used to evaluate the effect of FA on antioxidants, metal concentration (Fe, Zn, Cu, Cr, and Cd), photosynthetic pigments (chlorophyll a (chl‐a), chlorophyll b (chl‐b), total chlorophyll (total chl), and carotenoids), growth and yield performance. All antioxidants in roots, shoots and leaves of chickpea increase with increasing FA doses to combat FA stress. The activities of antioxidants were more in the root tissues to cope with stress induced in the plants as compared to shoot and leaf. Concentration of metals was found maximum in roots than the shoots and seeds. The highest concentration of Fe and lowest level of Cd were recorded in all treatments of FA for different parts of the plant. The treated crop showed reduced level of chlorophyll but enhanced level of carotenoids and protein. However, root length, number of nodules and biomass in 25 and 50% FA treatments did not differ significantly in comparison to respective control plants. These results suggest that heavy metals of FA causes oxidative stress in this crop and the antioxidant enzymes could help a pivotal role against oxidative injury.     

Spatio-temporal evaluation of event detection and measurement coherence among satellite rainfall products for ensembled dataset generation

Theoretical and Applied Climatology - Selection of a best suited satellite-based gridded rainfall product (SGRP) is challenging due to their significant variations at spatial and temporal scale....     

GPS-derived precipitable water vapour and its comparison with MODIS data for Almora, Central Himalaya, India

The present study is an attempt to analyse the precipitable water vapour (PWV) derived from Global Positioning System (GPS) and observed meteorological data over Almora, Central Himalayan Region. The PWV values derived using GPS study is compared with the corresponding moderate resolution imaging spectro-radiometer (MODIS) data. The statistical analysis reveals a positive correlation between both methods. Moderate resolution imaging spectroradiometer near-infrared (MODIS NIR) clear column water vapour product shows a higher correlation (R ² = 90–93 %) with GPS-derived precipitable water vapour on annual scale as compared to the seasonal scale (R ² = 62–87 %). MODIS is found to be overestimating in NIR clear column where the magnitude of bias and RMSE show systematic changes from season to season. Monsoon is an important phenomenon in the Indian weather context and holds significant importance in Central Himalayan ecosystem. The monthly and seasonal variation in precipitable water vapour is related with monsoon onset in the region. Diurnal variations in precipitable water vapour are studied with other meteorological data over Almora during dry and wet season. The precipitable water vapour had minimum value in the morning, increases in the afternoon to evening and again decreases to the midnight in both the dry and wet seasons. These results suggest that diurnal variation of water vapour is caused by the transport of water vapour by thermally induced local circulation.     

Long-term historic changes in climatic variables of Betwa Basin,India

In this study, trend analyses of historic past climatic variables were investigated for the Betwa basin located in Central India. In the serially independent climatic variables, Mann–Kendall test (MK test) was applied to the original sample data. However, in the serially correlated series, pre-whitening is used before employing the MK test. The long-term trend analysis showed several of the meteorological stations to exhibit a decreasing trend in annual and seasonal precipitation in the study area. Seasonal and yearly numbers of rainy days are decreased. However, onset of effective monsoon (except for Shivpuri and Tikamgarh stations) did not show any trend during the study period. For maximum temperature, five out of 12 stations showed a decreasing trend in monsoon season whereas almost all other stations showed an increasing trend in winter and no trend in summer season. For minimum temperature, only two stations of the basin showed a decreasing trend in monsoon and all other stations exhibited a significant increase in winter and summer season. The increase of winter temperature may adversely affect the growth of Rabi crop (wheat and mustard) in the study area. Potential evopotranspiration (PET) did not show any trend in monsoon, except for Jalaun and Jhansi stations, showing decreasing trends. Raisen and Vidisha stations showed an increasing trend in winter only, and the trend for other stations were random in nature. In summer, five out of 12 stations showed an increasing trend in PET. Results of this study can be employed in preparation of water resources development and management plan in the Betwa Basin.     

Mathematical analysis of a chaotic model in relevance to monsoon ISO

Mathematical analysis of a predominantly bimodal chaotic attractor, Lu system, was carried out to investigate a possible application of the model as a prototype of monsoon intra-seasonal oscillation (ISO). Bifurcation structures of the system are explored as the system parameter c and the forcing parameter F are varied. Stability criteria of equilibrium points of the forced Lu system are also explored in detail. A sensitivity study is carried out, by changing forcing parameter F, to explore relationships between some of the derived variables of the model and, based on such relationships, an empirical rule is used for extended range prediction. Analogous variables are also derived from the ISO data and prediction results are compared. Application of the prediction rule of regime transition to the observed ISO and chaotic model data is purely based on the bimodal characteristics of ISO and neglects some of the intricate mechanisms therein. We have found that a forced Lu system can be a good prototype in the prediction of peak anomaly of the monsoon ISO when growth rates around a threshold value are taken as predictors.     

Simulation of Air Quality using an ISCST3 Dispersion Model

Urban air quality is an issue of major concern across many cities in India. In particular, high levels of particulate matter (both SPM and RSPM) are responsible for noncompliance to air quality standards. Air quality modeling is an effective tool to simulate the air quality of a region and to predict air quality concentrations under different scenarios. Kanpur city which is a top‐ten urban conglomerate in India (based on population) is chosen for the application of the ISCST3 model and simulation of air quality. Sectored emission loads are estimated for transport, industrial, power, and domestic sectors, which provide an estimate of the major contributors to air pollution with specific reference to particulate matter, which is a major pollutant of concern. A detailed scenario analysis is carried out to estimate the changes in emissions that would take place due to various interventions. Dispersion modeling is carried out using the ISCST3 model, to estimate the concentrations of SPM all over the city under different scenarios. Emission inventory and meteorological data served as input to the model, and the air quality is predicted for various seasons and intervention scenarios. The modeled values for the scenario without intervention results in an underestimation of 48%, which is due to unaccountable or unidentified sources, trans‐boundary movement of SPM, and model calibration errors. To overcome the error, the model is calibrated with the observed values and results are obtained for other scenarios using the calibration factor. The paper demonstrates only the research direction currently used to simulate air quality in Indian cities. However, further refinement and research is required before it could be used for more accurate predictions.     

Use of objective analysis to estimate winter temperature and precipitation at different stations over western Himalaya

Temperature and fresh snow are essential inputs in an avalanche forecasting model. Without these parameters, prediction of avalanche occurrence for a region would be very difficult. In the complex terrain of Himalaya, nonavailability of snow and meteorological data of the remote locations during snow storms in the winter is a common occurrence. In view of this persistent problem present study estimates maximum temperature, minimum temperature, ambient temperature and precipitation intensity on different regions of Indian western Himalaya by using similar parameters of the neighbouring regions. The location at which parameters are required and its neighbouring locations should all fall in the same snow climatic zone. Initial step to estimate the parameters at a location, is to shift the parameters of neighbouring regions at a reference height corresponding to the altitude of the location at which parameters are to be estimated. The parameters at this reference height are then spatially interpolated by using Barnes objective analysis. The parameters estimated on different locations are compared with the observed one and the Root Mean Square Errors (RMSE) of the observed and estimated values of the parameters are discussed for the winters of 2007–2008.