How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region.

Although the effect of digital elevation model (DEM) and its spatial resolution on flood simulation modeling has been well studied, the effect of coarse and finer resolution image and DEM data on machine learning ensemble flood susceptibility prediction has not been investigated, particularly in data sparse conditions. The present work was, therefore, to investigate the performance of the resolution effects, such as coarse (Landsat and SRTM) and high (Sentinel-2 and ALOS PALSAR) resolution data on the flood susceptible models. Another motive of this study was to construct very high precision and robust flood susceptible models using standalone and ensemble machine learning algorithms. In the present study, fifteen flood conditioning parameters were generated from both coarse and high resolution datasets. Then, the ANN-multilayer perceptron (MLP), random forest (RF), bagging (B)-MLP, B-gaussian processes (B-GP) and B-SMOreg algorithms were used to integrate the flood conditioning parameters for generating the flood susceptible models. Furthermore, the influence of flood conditioning parameters on the modelling of flood susceptibility was investigated by proposing an ROC based sensitivity analysis. The validation of flood susceptibility models is also another challenge. In the present study, we proposed an index of flood vulnerability model to validate flood susceptibility models along with conventional statistical techniques, such as the ROC curve. Results showed that the coarse resolution based flood susceptibility MLP model has appeared as the best model (area under curve: 0.94) and it has predicted 11.65 % of the area as very high flood susceptible zones (FSz), followed by RF, B-MLP, B-GP, and B-SMOreg. Similarly, the high resolution based flood susceptibility model using MLP has predicted 19.34 % of areas as very high flood susceptible zones, followed by RF (14.32 %),B-MLP (14.88 %), B-GP, and B-SMOreg. On the other hand, ROC based sensitivity analysis showed that elevation influences flood susceptibility largely for coarse and high resolution based models, followed by drainage densityand flow accumulation. In addition, the accuracy assessment using the IFV model revealed that the MLP model outperformed all other models in the case of a high resolution imageThe coarser resolution image's performance level is acceptable but quite low. So, the study recommended the use of high resolution images for developing a machine learning algorithm based flood susceptibility model. As the study has clearly identified the areas of higher flood susceptibility and the dominant influencing factors for flooding, this could be used as a good database for flood management.

Impact of wetland fragmentation due to damming on the linkages between water richness and ecosystem services.

Evaluation of the importance of ecosystem services (ES) of various wetlands is well reported with global and regional level research, but the degree to which spatial-temporal variations in water richness (availability of water) have had an effect on ES has not yet been examined. The present work is intended to investigate the influence of wetland fragmentation due to damming on wetland water richness and the impact of changes in water richness on the ecosystem service value (ESV) of the wetland-dominated rivers of the lower Punarbhaba Basin, India, and Bangladesh, as the case. Water richness models of pre- and post-dam periods have been constructed based on four hydro-ecological parameters (hydro-period, depth of water, consistency of water appearance, and wetland size) following the semi-quantitative analytic hierarchy process (AHP). ESV of different wetland types, with and without considering water richness effect, has been computed. The result indicates that the overall wetland area decreased from 73,563 to 52,123 km2 during the post-dam period. Approximately 53.8% of the high water-rich region is decreased. Total wetland ESV has been lowered by 63.4% from 1989 to 2019, with an average reduction rate of 2%. This is mainly due to the squeezing of the wetland area during the post-dam period. If the impact of water richness on ESV is considered, the scenario is found to be very distinct. Total ESV of various ESV areas amounted to $33 million during the pre-dam period and is reduced to $19.71 million during the post-dam period. If compared to the total ESV of the wetland without considering the effect of water richness, the calculated ESV gap was $105 million in pre-dam and $38 million in post-dam period indicating a widening of the gap. Maintaining the ES of wetland hydrological management, specifically the flow maintenance of river and riparian wetlands, is essential.

Effects of lockdown due to COVID-19 outbreak on air quality and anthropogenic heat in an industrial belt of India.

Highly urbanized and industrialized Asansol Durgapur industrial belt of Eastern India is characterized by severe heat island effect and high pollution level leading to human discomfort and even health problems. However, COVID-19 persuaded lockdown emergency in India led to shut-down of the industries, traffic system, and day-to-day normal work and expectedly caused changes in air quality and weather. The present work intended to examine the impact of lockdown on air quality, land surface temperature (LST), and anthropogenic heat flux (AHF) of Asansol Durgapur industrial belt. Satellite images and daily data of the Central Pollution Control Board (CPCB) were used for analyzing the spatial scale and numerical change of air quality from pre to amid lockdown conditions in the study region. Results exhibited that, in consequence of lockdown, LST reduced by 4.02 °C, PM10 level decreased from 102 to 18 µg/m3 and AHF declined from 116 to 40W/m2 during lockdown period. Qualitative upgradation of air quality index (AQI) from poor to very poor state to moderate to satisfactory state was observed during lockdown period. To regulate air quality and climate change, many steps were taken at global and regional scales, but no fruitful outcome was received yet. Such lockdown (temporarily) is against economic growth, but it showed some healing effect of air quality standard.

COVID-19 pandemic persuaded lockdown effects on environment over stone quarrying and crushing areas.

Stone quarrying and crushing spits huge stone dust to the environment and causes threats to ecosystem components as well as human health. Imposing emergency lockdown to stop infection of COVID 19 virus on 24.03.2020 in India has created economic crisis but it has facilitated environment to restore its quality. Global scale study has already proved the qualitative improvement of air quality but its possible impact at regional level is not investigated yet. Middle catchment of Dwarka river basin of Eastern India is well known for stone quarrying and crushing and therefore the region is highly polluted. The present study has attempted to explore the impact of forced lockdown on environmental components like Particulate matter (PM) 10, Land surface temperature (LST), river water quality, noise using image and field derived data in pre and during lockdown periods. Result clearly exhibits that Maximum PM10 concentration was 189 to 278 µg/m3 in pre lockdown period and it now ranges from 50 to 60 µg/m3 after 18 days of the commencement of lockdown in selected four stone crushing clusters. LST is reduced by 3-5 °C, noise level is dropped to <65dBA which was above 85dBA in stone crusher dominated areas in pre lockdown period. Adjacent river water is qualitatively improved due to stoppage of dust release to the river. For instance, total dissolve solid (TDS) level in river water adjacent to crushing unit is attenuated by almost two times. When entire world is worried about the appropriate policies for abating environmental pollution, this emergency lockdown shows an absolute way i.e. pollution source management may restore environment and ecosystem with very rapid rate.

A novel approach for accurate identification of splice junctions based on hybrid algorithms.

The precise prediction of splice junctions as 'exon-intron' or 'intron-exon' boundaries in a given DNA sequence is an important task in Bioinformatics. The main challenge is to determine the splice sites in the coding region. Due to the intrinsic complexity and the uncertainty in gene sequence, the adoption of data mining methods is increasingly becoming popular. There are various methods developed on different strategies in this direction. This article focuses on the construction of new hybrid machine learning ensembles that solve the splice junction task more effectively. A novel supervised feature reduction technique is developed using entropy-based fuzzy rough set theory optimized by greedy hill-climbing algorithm. The average prediction accuracy achieved is above 98% with 95% confidence interval. The performance of the proposed methods is evaluated using various metrics to establish the statistical significance of the results. The experiments are conducted using various schemes with human DNA sequence data. The obtained results are highly promising as compared with the state-of-the-art approaches in literature.

Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system.

This work presents more precise computational methods for improving the diagnosis of Parkinson's disease based on the detection of dysphonia. New methods are presented for enhanced evaluation and recognize Parkinson's disease affected patients at early stage. Analysis is performed with significant level of error tolerance rate and established our results with corrected T-test. Here new ensembles and other machine learning methods consisting of multinomial logistic regression classifier with Haar wavelets transformation as projection filter that outperform logistic regression is used. Finally a novel and reliable inference system is presented for early recognition of people affected by this disease and presents a new measure of the severity of the disease. Feature selection method is based on Support Vector Machines and ranker search method. Performance analysis of each model is compared to the existing methods and examines the main advancements and concludes with propitious results. Reliable methods are proposed for treating Parkinson's disease that includes sparse multinomial logistic regression, Bayesian network, Support Vector Machines, Artificial Neural Networks, Boosting methods and their ensembles. The study aim at improving the quality of Parkinson's disease treatment by tracking them and reinforce the viability of cost effective, regular and precise telemonitoring application.

Accurate prediction of coronary artery disease using reliable diagnosis system.

This paper presents more accurate and reliable computational methods for aiding the treatment of people with coronary artery disease. New techniques are introduced for improved evaluation and distinguish cardiac disease affected patients from the healthy controls. Experiments are conducted with high level of error tolerance rate and confidence level at 95% and 99% and established the results with corrected T-tests based on comparison of various performance measures. Normal kernel density estimator is used for visual distinction of cardiac controls. A new ensemble learning method comprising of Bayesian network as classifier and Principal components method as the projection filter with ranker search is used for the relevant feature selection. Analysis of each model is performed and discusses major findings and concludes with promising results compared to the related works. Multiple Correspondence analysis is used for exploring heart disease variable's relationships. Robust machine learning algorithms used are Rotation forests, MultiBoosting, Sparse multinomial logistic regression for better performance with fine tuning of their involved parameters. The work aims at improving the software reliability and quality of diagnosis of cardiac disease with robust inference system. To the best of our knowledge, from the literature survey, experimental results presented in this work show best results with supportive statistical inference.