Group velocity dispersion analysis in northern Peninsular Malaysia

The crustal structure beneath three seismic stations over Malaysia has been investigated with the application of the group velocity dispersion analysis of the northern Sumatra earthquake data which occurred on 06 April 2010. Eighteen crustal layer models are constructed to assess the structure. Group velocity dispersions have been computed for the recorded earthquake data using a graphical method and modified Haskell matrix method for the models. Both dispersions have been presented for the interpretation of crustal layers. Findings have shown four major crustal layers having thicknesses of 2.5–4.0, 2.0–5.5, 5.0–8.0, and 8.5–9.0 km, while in Terengganu, it has shown three layers. Density, shear, and compressional wave velocities used in models have suggested that the crustal structure of the northern part of Peninsular Malaysia is crystalline. Major crustal minerals are of quartz, plagioclase, and mica. Most layers seem to have upward directions toward Perak from Kedah and Terengganu.     

Impact analysis of climate change on Kolahoi Glacier in Liddar Valley,north-western Himalayas

Glaciers are among the most conspicuous and dynamic features on the earth’s surface and are also highly sensitive to changes in climatic parameters. Glaciers in the Kashmir Himalayas have been reported to be retreating due to climate forcing. Kolahoi Glacier is one of the largest and important glaciers of the Kashmir Himalayas and is the main source of Liddar River, which is the largest tributary of the Jhelum River system. In the present study, an analysis to assess the response of Kolahoi Glacier to the changing climate was carried out using the Survey of India (SoI) map and multi-temporal Landsat satellite data. The results show a significant change in the spatial extent of Kolahoi Glacier. The total area of this glacier has reduced from 12.21 km² in 1962 to 11.61 km² in 2010. An analysis of meteorological data (temperature and precipitation) shows that the average annual temperature increased from 9.1 °C in 1980–1989 to 10.3 °C in 2000–2009, while the precipitation decreased from 1329.44 to 1126.89 mm during the same period. The results suggest that this glacier will be annihilated completely if the same retreating trend continues.     

Macrophytes’ Performance in Floating Treatment Wetlands for the Remediation of Ciprofloxacin-Contaminated Water

The presence of trace antibiotics in domestic and industrial effluents poses a risk of toxicity to fauna and flora. The application of floating treatment wetlands (FTWs) is a low-cost and sustainable approach to remediate wastewater. In this study, the performance of different macrophytes vegetated in FTWs for the remediation of ciprofloxacin (CIP)-contaminated water is investigated. Six macrophytes, Brachia mutica, Typha domingensis, Phragmites australis, Canna indica, Cyperus laevigatus, and Leptochloa fusca, are vegetated in FTWs for the removal of CIP (50 mg L⁻¹) from water. The FTWs show the potential to remove 30–43.58 mg L⁻¹ CIP from water in 28 days. They also reduce the chemical oxygen demand (118–138 mg L⁻¹) and biochemical oxygen demand (35–45 mg L⁻¹) of water. Among the macrophytes, C. indica removes maximum (43.58 mg L⁻¹) and T. domingensis minimum (30 mg L⁻¹) CIP. Canna indica and T. domingensis exhibit the maximum and minimum increase (30% and 12% of dry biomass) in growth, respectively. This study reveals that the FTWs vegetated with different plant species exhibit varying performances in removing CIP from water. This investigation is a step forward toward sustainable bioremediation of water contaminated with antibiotics.     

Classification of rocks radionuclide data using machine learning techniques

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of ²³²Th, ²²⁶Ra and ⁴⁰K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.     

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.