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Abstract Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stage. Hence, clinical records may play a vital role to diagnose this disease at the correct stage for suitable medication to cure it. To detect lung cancer an accurate prediction method is needed which is significantly reliable. In the digital clinical record era with advancement in computing algorithms including machine learning techniques opens an opportunity to ease the process. Various machine learning algorithms may be applied over realistic clinical data but the predictive power is yet to be comprehended for accurate results. This paper envisages to compare twelve potential machine learning algorithms over clinical data with eleven symptoms of lung cancer along with two major habits of patients to predict a positive case accurately. The result has been found based on classification and heat map correlation. K-Nearest Neighbor Model and Bernoulli Naive Bayes Model are found most significant methods for early lung cancer prediction.

The study is carried out to investigate the surface runoff depth with changing precipitation due to climate change in the study area where sandy loam and loamy soil are dominant. In this study, future rainfall is projected by a statistical downscaling model (SDSM) using a set of predictors derived from a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) [the Norwegian Earth System Model (NorESM)] with updated scenarios SSP 4.5 and SSP 8.5. Daily rainfall values for the observed period (1981 to 2014) are validated using statistical learning and evaluated with matrices, namely, root mean square error (RMSE), coefficient of correlation, and Nash–Sutcliffe efficiency (NSE), which are found to be valid for further predictions. Rainfall projections show a decrease in rainfall trend of 50% from 2030 to 2040 for scenario SSP 4.5 and an increase of 7% from 2040 to 2050. Predicted rainfall for scenario SSP 8.5 shows a similar trend of decreasing rainfall of 24% for the period 2030–2040 and an increase of 19% in the period 2040–2050. Furthermore, these rainfall values are spatially modelled in a geographic information system (GIS) and rainfall maps are obtained. The obtained rainfall map, land-use map, and soil map are overlaid to compute curve numbers and runoff depths. A similar trend of decrease in runoff is observed for the period 2030–2050. The overall trend of climate change shows a water-stressed scenario. HIGHLIGHTS The study downscaled a CMIP6 GCM to evaluate the effect of climate change on rainfall.; The study conducts hydrological assessments to predict surface runoff for future scenarios.; The SDSM model, when subjected to statistical learning analysis, shows good performance in simulating rainfall values.; The study predicts a decrease in rainfall and runoff in the years 2030, 2040, and 2050.; Machine learning-boosted SDSM is a strong tool.;

Abstract Seismic inversion involves extracting qualitative as well as quantitative information from seismic reflection data that can be analyzed to enhance geological and geophysical interpretation which is more subtle in a traditional seismic data interpretation. Among many approaches that have been made to improve interpretation of post-stack seismic data, a great effort has been made to use maximum likelihood (ML), sparse spike inversion (SSI) along with multi-attribute analysis (MAA) aimed to increase the resolution power of interpreting seismic reflection data and mapping into the subsurface lithology. These methods are applied to the Blackfoot seismic reflection data to estimate reservoir. The methods were first applied to the composite trace close to well locations and were inverted for acoustic impedance (AI). The results depict that the inverted AI matches very well with the well log AI. The statistical analysis demonstrates good performance of the algorithm. Thereafter, the entire seismic section was inverted to acoustic impedance section. The analysis of the inverted impedance section shows an anomaly zone in between 1060 and 1075 ms time and characterize it as reservoir. Further, the multi-attribute analysis is performed to estimate porosity and density in the inter-well region. The inverted porosity section shows a high porosity anomaly and a low density anomaly in between 1060 and 1075 ms time intervals which corroborated well with the low impedance zone and confirm the presence of a reservoir.

Visibility of Ramganga river course change detection was made using Remote Sensing and GIS in study area among period of forty-one years from 1972 to 2013. Landsat MSS, TM, ETM+, LISS-III satellite images from 1972, 1989, 2000, 2006, and 2013 respectively were used to delineate the historical changes of the river course. This study shows that for a long time this area has been suffering due to erosion problem and shifting characteristics of the Ramganga River. The Ramganga river course has been shifting and the overall shifting is towards the south-west direction in different places which leads to the village erosion. The area has remained undeveloped due to infrastructure damaged by flood, changing course. This study may be helpful for the overall river management and planning for future prevention of food, changing coursing, loss of properties.