Evaluating the performance of individual and novel ensemble of machine learning and statistical models for landslide susceptibility assessment at Rudraprayag district of Garhwal Himalaya

© 2020 by the authors. Landslides are known as the world's most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling

Unsteady natural convection within an attic-shaped space subject to sinusoidal heat flux on inclined walls

© 2020, Tech Science Press. All rights reserved. Free convection inside an attic enclosure in which sinusoidal heat flux applied on the inclined walls and a constant temperature applied on the base wall has been investigated numerically to demonstrate the primary flow characteristics and heat transfer within the attic enclosure over daily routine cycles. To solve the governing equations, the finite volume technique has been utilized. After performing the grid independency and time step size tests, the roles of Rayleigh number (Ra) and the attic aspect ratio (AR) on the unsteady flow structure and heat transfer phenomenon are explained for a constant Prandtl number (0.72) for the air. Results are illustrated as a form of stream function and isotherms. Moreover, heat transfer is calculated in terms of Nusselt number. The numerical simulations reveal stratified flow within the enclosure during the daytime nonlinear heating stage. However, during night-time, nonlinear cooling stage the flow turns into unstable as the forms of rising and sinking plumes for sufficiently higher Rayleigh number

Transient free convection and heat transfer in a partitioned attic-shaped space under diurnal thermal forcing

One primordial consideration in residential ventilation standards is the comfort of provided to people living in those habitations. This is highly dependent on the thermal and fluid flow conditions, the space geometry and so on. Efficient designs may reduce the energy usage, making the buildings more sustainable over a longer period of time. This study aims to investigate the impact of whole day thermal conditions on the fluid flow structure and heat transfer phenomena, mainly natural convection, inside a partitioned attic-shaped configuration. The Finite Volume Method is applied to solve the governing equations. Sinusoidal thermal boundary condition is applied on the sloping walls to illustrate the characteristics of primary flow through daily cycles. A highly thermal conductive partition was placed vertically at the middle of the cavity. Note that through the partition, only heat could freely transfer between two fluid zones. Results show that, during day time, a stratified fluid flow structure is obtained, which originates from the prevailing conduction heat transfer mechanism, while, for the night-time it changed into a strong convection mechanism which significantly affects the flow structure. These results are particularly important for understanding the fluid dynamics inside the attic shaped building and also designing new residential building

Impact of COVID-19 induced lockdown on environmental quality in four Indian megacities Using Landsat 8 OLI and TIRS-derived data and Mamdani fuzzy logic modelling approach

© 2020 by the authors. The deadly COVID-19 virus has caused a global pandemic health emergency. This COVID-19 has spread its arms to 200 countries globally and the megacities of the world were particularly affected with a large number of infections and deaths, which is still increasing day by day. On the other hand, the outbreak of COVID-19 has greatly impacted the global environment to regain its health. This study takes four megacities (Mumbai, Delhi, Kolkata, and Chennai) of India for a comprehensive assessment of the dynamicity of environmental quality resulting from the COVID-19 induced lockdown situation. An environmental quality index was formulated using remotely sensed biophysical parameters like Particulate Matters PM10 concentration, Land Surface Temperature (LST), Normalized Different Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). Fuzzy-AHP, which is a Multi-Criteria Decision-Making process, has been utilized to derive the weight of the indicators and aggregation. The results showing that COVID-19 induced lockdown in the form of restrictions on human and vehicular movements and decreasing economic activities has improved the overall quality of the environment in the selected Indian cities for a short time span. Overall, the results indicate that lockdown is not only capable of controlling COVID-19 spread, but also helpful in minimizing environmental degradation. The findings of this study can be utilized for assessing and analyzing the impacts of COVID-19 induced lockdown situation on the overall environmental quality of other megacities of the world

Differential expression analysis for sequence count data

*Motivation:* High-throughput nucleotide sequencing provides quantitative readouts in assays for RNA expression (RNA-Seq), protein-DNA binding (ChIP-Seq) or cell counting (barcode sequencing). Statistical inference of differential signal in such data requires estimation of their variability throughout the dynamic range. When the number of replicates is small, error modelling is needed to achieve statistical power.

*Results:* We propose an error model that uses the negative binomial distribution, with variance and mean linked by local regression, to model the null distribution of the count data. The method controls type-I error and provides good detection power. 

*Availability:* A free open-source R software package, _DESeq_, is available from the Bioconductor project and from "http://www-huber.embl.de/users/anders/DESeq":http://www-huber.embl.de/users/anders/DESeq

Electrochemical synthesis of peroxomonophosphate using boron-doped diamond anodes

A new method for the synthesis of peroxomonophosphate, based on the use of boron-doped diamond electrodes, is described. The amount of oxidant electrogenerated depends on the characteristics of the supporting media (pH and solute concentration) and on the operating conditions (temperature and current density). Results show that the pH, between values of 1 and 5, does not influence either the electrosynthesis of peroxomonophosphate or the chemical stability of the oxidant generated. Conversely, low temperatures are required during the electrosynthesis process to minimize the thermal decomposition of peroxomonophosphate and to guarantee significant oxidant concentration. In addition, a marked influence of both the current density and the initial substrate is observed. This observation can be explained in terms of the contribution of hydroxyl radicals in the oxidation mechanisms that occur on diamond surfaces. In the assays carried out below the water oxidation potential, the generation of hydroxyl radicals did not take place. In these cases, peroxomonophosphate generation occurs through a direct electron transfer and, therefore, at these low current densities lower concentrations are obtained. On the other hand, at higher potentials both direct and hydroxyl radical-mediated mechanisms contribute to the oxidant generation and the process is more efficient. In the same way, the contribution of hydroxyl radicals may also help to explain the significant influence of the substrate concentration. Thus, the coexistence of both phosphate and hydroxyl radicals is required to ensure the generation of significant amounts of peroxomonophosphoric acid

Comparison between deep learning and tree‐based machine learning approaches for landslide susceptibility mapping

The efficiency of deep learning and tree‐based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree‐based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning