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Ex-situ conservation places such as botanical gardens require sufficient soil quality to support introduced species from various phytogeographical zones. The soil quality of the Botanic Garden of Indian Republic (BGIR), Noida, Uttar Pradesh, was evaluated to quantify soil nutrients. The dependency of one nutrient on the other nutrients was investigated using Pearson correlation and Multilinear regression analysis (MLRA). At the 0.05 level of significance, the nutrients Log10S and Log10EC (r = 0.97), N and OC (r = 0.98), Mn and OC (r = 0.97), Mn and N (r = 0.92), Ca and pH (r = − 0.91), Cu and Fe (r = 0.94) were found to be associated. Correspondence Analysis (C.A.) has been performed to find the association of soil elements with the soil type of study site. The spatial indices like NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), NPCRI (Normalized Pigment Chlorophyll Index), RDVI (Renormalized Difference Vegetation Index) have shown significant correlation with the Log10S, Mg, Log10Zn, B and Fe respectively (with respective Pearson correlation coefficient r = 0.88, r = − 0.90, r = − 0.93, r = 0.91, r = 0.92 at P 10Zn (r = − 0.89, r = − 0.88 r = 0.92 at P r = 0.91, r = 0.93 respectively). MSAVI is negatively interrelated with Ca (r = − 0.89), SCI is negatively interrelated with Log10 K (r = − 0.98), BSI (Bare Soil Index) is positively associated with pH (r = 0.91), and negatively with Ca (r = − 0.93). At the same time, other indices like SAVI (Soil Adjusted Vegetation Index), SATVI (Soil Adjusted Total Vegetation Index), NDWI (Normalized Difference Water Index), and DVI (Difference Vegetation Index) have failed to explain the presence of soil nutrients based on spectral reflectance. This study is important for understanding the changing nutrient status of soil at the conservation site for successfully establishing plants from different phytogeographical zones.

The Sundarban Biosphere Reserve (SBR) in India is highly vulnerable to cyclones and tidal surges due to its geographical location and low-lying coastal morphology. This deltaic tract experiences frequent cyclones that incur loss of life and property. Despite this, the SBR lacks a systematic and effective disaster warning/management plan that benefits all residents/stakeholders. Hence, it is imperative to assess the mitigation measures currently in place to effectively counter the risks arising from an increasing frequency of tropical cyclones therein. This paper examines the efficiency of the existing early warning systems (EWS) and emergency preparedness in the SBR. Data was collected at the household level on EWS, community preparedness and the economic losses occurring due to cyclones in the last ten years through field surveys. The Poisson distribution model was used to understand the relation of losses with the EWS. Our results reveal that a lack of early warning awareness and delays in emergency preparedness have made coastal communities more vulnerable. The SBR lacks an adequate disaster management plan for minimizing such losses and policy interventions are urgently required to safeguard coastal communities during and in the aftermath of cyclones. Possible improvements in existing strategies for reducing cyclone vulnerability are suggested.

Gully erosion is one of the advanced forms of water erosion. Identifying the effective factors and gully erosion predicting is one of the important tools to control and manage such phenomenon. The main purpose of this study is to evaluate the effect of four different resampling algorithms including cross-validation (5-fold and 10-fold) and bootstrapping (Bootstrap and Optimism bootstrap) on boosted regression tree (BRT), support vector machine (SVM), and random forest (RF) models in spatial modeling and evaluation of head-cut gully erosion in Konduran watershed. For this purpose, based on an extensive field survey, the points of the head-cut of the gully erosion were identified first, and a map of the distribution of head-cut gully erosion in the study area was prepared. Then 18 variable identify and prepare as factors affecting the occurrence of head-cut gully erosion. To assess the efficiency of the models, receiver operating characteristics (ROC) and area under the curve (AUC) were used. The results of the assessment indicated that the use of resampling algorithms increases the efficiency of the models. The integrated optimism-bootstrap-BRT, optimism-bootstrap-SVM, and Optimism-Bootstrap-RF models with AUC 0.85, 0.823 and 0.89 respectively, outperformed the cross-validation 5fold (BRT, SVM, RF), Cross-validation 10fold (BRT, SVM, RF) and Bootstrap (BRT, SVM, RF) integrated algorithms.

The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad.

Ecosystem services provided by wetlands are essential for communities living near wetlands, especially in an underdeveloped semi-arid landscape. The land use land cover changes and ecosystem degradation and water quality change over the past few decades have had immense effects on declining wetland ecosystem services. With the degradation, it is exerting superfluous effects on wetland communities including loss of livelihood, and decline in other wetland services like fishing, aquaculture, fuelwood, fodder, and many more. The present study attempts to assess the changing nature of wetland health, water quality, and declining ecosystem services of Mount Abu wetlands in Rajasthan, India. For assessing the change of wetland extent, we have used the remote sensing-based data for preparation of land use land cover change from 1992 to 2020. The water samples have been collected from the wetland, and different biophysical parameters of the water have been tested in the laboratory. A questionnaire-based household survey has been conducted to understand the perception of the wetland communities on the loss of ecosystem services over three decades. Further, a correlation and cluster assessment has been conducted to understand the degradation of wetland health in the selected wetlands. The study results indicated deteriorating conditions of wetland health and declining ecosystem services in the study area over the time periods. The land use land cover change analysis indicated a decrease in the spatial extent of the wetlands in the study area. Wetland communities are being affected due to the degradation of wetland health. The study recommended executing a wetland management plan for long-term conservation and livelihood management for the Mount Abu wetlands and communities.

Climate change is strongly affecting the Himalayas. Geologically young and fragile, the Himalayas are sensitive to even minor changes in the climate. Regional warming in Himalayas has been observed between 0.15 and 0.60°C per decade, which is higher than the mean global warming rate of 0.74°C per 100years. Consequent to this warming trend, the glaciological changes in Himalayas are obvious, which has resulted in the formation, expansion and disappearance of various types of glacial lakes. The dangerous lakes among these pose threat to downstream community and infrastructure. In this study, an attempt has been made to critically analyze the progress in Glacier Lake Outburst Floods (GLOF) research and understand its dynamism from multiple approaches through a meta-analysis of peer-reviewed scholarly literature for a period of 2001–2020. The study has found that the glacial lakes in the study region are increasing in number and expanding rapidly owing to the climate change and anthropogenic causes as the Glacier Lake Outburst Floods (GLOFs) are becoming common. The expansion rates of pro-glacial lakes connected to glaciers and moraine-dammed lakes are faster than other type of lakes. Findings from the studies on area change analysis in the region reveal that a number of expanding glacial lakes can emerge as potential sites for future GLOFs, hence need immediate monitoring and observation. Geospatial tools and techniques coupled with field investigations have been found as a potent tool for mapping the evolution and propagation of GLOF hazards resulting from accelerated shrinking and thinning of glaciers and continued lake expansion. Further, the satellite-based remote sensing and modeling has been found as an excellent tool for GLOF management as well as reconstruction of previous GLOF events and prediction of future outburst potential. The idea of this study is linked to the increased incidence of GLOF events in the Himalayan region. This study will help in better understanding of glacial lake expansion and provide scope for future research in devising risk management action plans of potential GLOFs by selecting expanding glacial lakes as case studies.

The Forest Stewardship Council developed the concept of High Conservation Values (HCVs) as a criteria in the forest certification process in order to promote sustainable forest management. It has six major components or values and component one and two of HCVs deal with the habitat for viable populations of “rare, endemic and threatened (RET) species” using the IUCN Red List category and other national / regional / local lists. But a consistent robust methodology for identification of these areas, does not exist. The present study tried to develop for the first time, a straight forward inclusive methodology for identification of HCVAs for the RET species on a spatio-temporal scale. A total of 50 RET and other significant species (32 flora, 10 fauna and 8 avifauna) were identified after a thorough literature review, field surveys and consultations with experts. Occurrence data of the selected species was collected from different secondary sources, field surveys, institutes and scientists who have worked on them. A 10 km grid-based approach and stratified random sampling was used for the primary GPS field surveys conducted during 2018–2019. MaxEnt species distribution model (SDM) software was used based on the occurrence data and environmental variables for identification of potential suitable habitats for the selected species. Linear support vector machine (LSVM) model was used for assessing the performance of the SDMs. The performance of each SDM has been validated through Cohen's Kappa (KAPPA), true skill statistic (TSS) and receiver operating characteristics (ROC) models. The proposed methodology addresses the urgent need for a holistic and robust set of techniques to apply the HCV toolkit. This is key to identify and map HCVAs for RET species at the landscape level and can be easily adapted to and adopted at the national, regional, state or local level in India. The methods offer an efficient, reliable approach for the application of the HCV concept, elsewhere in the world.

This study used MaxEnt model to determine the potential distribution of M. longifolia, B. lanzan, E. officinalis, T. bellirica, T. chebula and S. urens under current climatic conditions and subsequently map the their potential future distribution under four representative concentration pathways for 2050 and 2080 in the Madhya Pradesh, India. The results showed that rainfall of wettest (Bio_16) and driest (Bio_17) quarters together contributes nearly 60% to the total variations under all RCP scenarios. The results indicated that under current climatic conditions south eastern region of Madhya Pradesh has higher potential of all the studied species. M. longifolia will be able to withstand the future climate change, while B. lanzan and T. chebula will be impacted negatively. T. bellirica and E. officinalis are expected to expand to new areas under RCP 4.5 in near-term future. S. urens is expected to gain area under RCP 2.6 and 4.5 for 2080. All species are expected to show shift in range towards wetter forest type in north eastern region of the state. Our results will be useful for identifying potential growth regulating factors and suitable areas to support scientific management of these economically important NTFP species. These results can also be useful in gauging the behaviour of other associate species in the area with similar bioclimatic requirements.

In this paper, we developed highly accurate ensemble machine learning models integrating Reduced Error Pruning Tree (REPT) as a base classifier with the Bagging (B), Decorate (D), and Random Subspa...

In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi district of India through the developmentof different novel GIS based soft computing approaches namel...

Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high intensity and kinetic energy over short time periods. So, keeping this scenario in view, the main objective of this work is to identify areas vulnerable to soil erosion and propose the most suitable model for soil erosion susceptibility in subtropical environment. The implementation of machine learning and artificial intelligence techniques with a GIS environment for determining erosion susceptibility is highly acceptable in terms of optimal accuracy. The point-specific values of different elements from random sampling were considered for this study. Sensitivity analysis of the predicted models (i.e., analytical neural network, geographically weighted regression and GWR–ANN ensemble) was performed using the maximum causative factors and related primary field observations. The area under curve of receiver operating system reveals precision with 87.13, 89.57 and 91.64 for GWR, ANN and ensemble GWR–ANN, respectively. The ensemble GWR–ANN is more optimal than the GWR, ANN for determining water-induced soil erosion susceptibility. The process of soil erosion is not a unidirectional process, so the multidimensional impacts from the conditioning factors have to be determined precisely by considering the maximum possible factors as well as selecting optimal models for specific regions.

Increasing global population pressure and related proliferations in demands for resources which eventually resulted in sensitive pressure on regions covering valued biodiversity. Human–wildlife conflict is one of the most common issues in conservation, encircling a huge diversity of circumstances and species. However, reasons of conflict are frequently more complex than predicted and issues which can affect human behaviour in these circumstances need to be implicit. The Leopard (Panthera pardus fusca) populations are being isolated in their micro habitat due to landscape fragmentation and increasing anthropogenic pressure over the India. Therefore, encounters between Leopards, livestock and humans are increasing in many areas, rising concerns about the costs of Leopards conservation. The present study aimed to assess of human–leopard conflict in the Aravalli landscape of Haryana (Gurugram, Mewat and Faridabad districts), India. The study was undertaken to investigate the ecological aspects of human–leopard conflict through spatial characteristics and dynamics of such conflict. In the present study, GPS field survey and mapping were done for the conflict’s sites in three districts of Haryana by the reference of newspaper articles, news reports and internet sources. Afterwards, GPS field survey was carried out to verify conflict sites in the landscape. To understand ecological aspects of human–leopard conflict, the result shows that forest cover in the Aravalli landscape has been decreased continuously from 1996 to 2018. It has been also recorded that barren rocky land has been reduced in the given time period. In contrast, agriculture and settlement have been increased dramatically from 1996 to 2018. The change matrix analyses show that forest cover and barren rocky land has been converted in agriculture land as well as human settlements. In the study area, it has also been found that water bodies have also been declined. For Susceptibility zonation, 10 parameters were selected and prepared by help of literature review and validated using linear support vector machine model. Maxent model was run for 30-m grain size and output suggested a susceptibility zone for human–leopard conflict in the Gurugram, Faridabad and Mewat. The study concludes that the human–leopard conflicts were mostly recorded in the area which has been converted from forest land to agriculture or settlements and they were found to be most susceptible. The human–leopard conflicts were also recorded at the edge of forested land in study area.

Coastal districts of India being diverse in nature are under constant vulnerability to hazards. The article analyzes vulnerability as a function of exposure, sensitivity and adaptation capacity. We selected site-specific factors of exposure, sensitivity and adaptation for constructing the composite vulnerability index. Findings revealed very high and high vulnerability in the eastern coastal districts while western coastal districts experienced moderate and low vulnerability. High frequency of cyclone and storm surge, low socio-economic status and lack of basic amenities and facilities were found significant stressors for increasing vulnerability in coastal districts. Composite vulnerability index not only helped in analyzing causative factors of vulnerability but also proved to be an effective tool in identifying priority districts where efforts could be made to lessen vulnerability. The study calls for policy measures to reduce vulnerability and safeguard the coastal communities from the risk of disasters.

Climate variability and continual occurrence of cyclones, flood and storm surge have greater implications on fragile ecosystem of Indian Sundarban Biosphere Reserve (SBR). Centurial meteorological data were used to assess the climate variability in the Reserve. Multivariate principal component analysis was performed to identify the consistency among the conditioning parameters of climate. Cyclones, severe cyclone, storm surge height, salinity intrusion, pH and surface water temperature were assessed to figure out the overall consequences of climate variability. Field investigation from 570 households from coastal and inland blocks (administrative division of the district) was carried out to reaffirm the variability in climatic conditions and its environmental, economic and social consequences in SBR. Study revealed wide spatiotemporal variation in temperature and rainfall. Rise in sea level, flood and high storm surge height and salinity intrusion were inducing vulnerability in the coastal blocks. Communities in Gosaba, Kultali, Kakdwip, Sagar, Patharpratima and Namkhana blocks largely showed high level of agreement for climate variability. Hence, the study calls for effective adaptation and mitigation strategies.

Drought is a natural phenomenon posing severe implications for soil, groundwater and agricultural yield. It has been recognized as one of the most pervasive global change drivers to affect the soil. Soil being a weakly renewable resource takes a long time to form, but it takes no time to degrade. However, the response of soil to drought conditions as soil loss is not manifested in the existing literature. Thus, this study makes a concerted effort to analyze the relationship between drought conditions and soil erosion in the middle sub-basin of the Godavari River in India. MODIS remote sensing data was utilized for driving drought indices during 2000-2019. Firstly, we constricted Temperature condition index (TCI) and Vegetation Condition Index (VCI) from Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from MODIS data. TCI and VCI were then integrated to determine the Vegetation Health Index (VHI). Revised Universal Soil Loss Equation (RUSLE) was utilized for estimating soil loss. The relationship between drought condition and vegetation was ascertained using the Pearson correlation. Most of the northern and southern watersheds experienced severe drought condition in the sub-basin during 2000-2019. The mean frequency of the drought occurrence was 7.95 months. The average soil erosion in the sub-basin was estimated to be 9.88 t ha (1) year (1). A positive relationship was observed between drought indices and soil erosion values (r value being 0.35). However, wide variations were observed in the distribution of spatial correlation. Among various factors, the slope length and steepness were found to be the main drivers of soil erosion in the sub-basin. Thus, the study calls for policy measures to lessen the impact of drought and soil erosion. (C) 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.

The present study led to setting up a grid-based soil fertility map along with the best fit model in the coastal regions based on soil physical (coarse, sand, silt, clay, bulk density), chemical (CEC, pH, and soil organic carbon), topographic (elevation), and nutrient elements (P

The mass accumulation of population in the larger cities of India has led to accelerated and unprecedented peripheral urban expansion over the last few decades. This rapid peripheral growth is characterized by an uncontrolled, low density, fragmented and haphazard patchwork of development popularly known as urban sprawl. The Kolkata Metropolitan Area (KMA) has been one of the fastest-growing metropolitan areas in India and is experiencing rampant suburbanization and peripheral expansion. Hence, understanding urban growth and its dynamics in these rapidly changing environments is critical for city planners and resource managers. Furthermore, understanding urban expansion and urban growth patterns are essential for achieving inclusive and sustainable urbanization as defined by the United Nations in the Sustainable Development Goals (e.g., SDGs, 11.3). The present research attempts to quantify and model the urban growth dynamics of large and diverse metropolitan areas with a distinct methodology considering the case of KMA. In the study, land use and land cover (LULC) maps of KMA were prepared for three different years (i.e., for 1996, 2006, and 2016) through the classification of Landsat imagery using a support vector machine (SVM) classification approach. Then, change detection analysis, landscape metrics, a concentric zone approach, and Shannon’s entropy approach were applied for spatiotemporal assessment and quantification of urban growth in KMA. The achieved classification accuracies were found to be 89.75%, 92.00%, and 92.75%, with corresponding Kappa values of 0.879, 0.904, and 0.912 for 1996, 2006, and 2016, respectively. It is concluded that KMA has been experiencing typical urban sprawl. The peri-urban areas (i.e., KMA-rural) are growing rapidly, and are characterized by leapfrogging and fragmented built-up area development, compared to the central KMA (i.e., KMA-urban), which has become more compact in recent years.

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These landslides can be deadly to the community living downslope with their fast pace, turning failures into catastrophic debris flows and avalanches. Active tectonics coupled with rugged topography in a complex geoenvironment multiplies this likelihood. The available hazard maps are usually helpful in mitigating disasters. However, fool-proof predicting landslide susceptibility identification remains a challenge in landslide discipline. Recently, ensemble machine learning (ML) techniques have proved the potential to provide a more accurate and efficient solution in spatial modeling. The main purposes of the current study are to examine and evaluate the predictive capability of support vector machine hybrid ensemble ML algorithms, i.e., the bagging, boosting, and stacking for modeling the catastrophic rainfall-induced landslide occurrences in the Northern parts of Kyushu Island, at the watershed scale in Japan. In this study, a landslide inventory map containing 265 landslide polygons was first interpreted from the aerial photographs and fieldwork after the September 2017 rainfall event. The raw data were randomly separated into two parts using a 70/30 sampling strategy for training and validating the landslide models. Then, 13 predisposing factors were prepared as predictors and dependent variable. The landslide susceptibility maps (LSM) were validated by the area under the receiver operating characteristic curve (AUC). The results of validation showed that the AUC values of the four models (SVM-Stacking, SVM, SVM-Bagging, and SVM-Boosting) varied from 0.74 to 0.91. The SVM-boosting model outperformed the other models, while SVM-stacking model has found to be the lowest performance. The outcome suggests that an ensemble ML model does not necessarily mean good performance. It is always preferable to select an appropriate model, such as the one proposed the hybrid novel ensemble SVM-boosting model, which could significantly improve the accuracies of LSM. Also, from Information Gain Ratio (IGR) we found that the rainfall factor mainly affects the results, that agrees with the analogy of present study.

This study investigated the characteristics of rainfall-triggered landslides during the Typhoon Bilis in the Dongjiang Reservoir Watershed, China. The comparative shallow landslide susceptibility mappings (LSMs) were produced by the ensemble data-driven statistical models in a GIS environment. At first, the landslide inventory for the study area was prepared from the high-resolution QuickBird images, and China–Brazil Earth Resources Satellite images, and field survey. Other necessary data for landslide susceptibility analysis such as the amount of rainfall, geology, and topography were also collected from the respective agencies. Twelve predisposing factors were then prepared using this available dataset. To reduce the subjectivity of models and caution in the selection of predisposing factors, and to avoid the spatial autocorrelation redundancy, certainty factor approach was attempted to optimize these twelve set of parameters. For validating the accuracy of the model, the original landslide data were randomly divided into two parts: 70% (1545 landslides) for training the model and the remaining 30% (662 landslides) for validation. The verified results showed that using the optimized predisposing factors has a higher performance than using all the original twelve factors. The results of ensemble models also showed that LSM maps prepared using binary logistic regression (accuracy is 0.848) model are more accurate than those prepared using bivariate statistical analysis (accuracy is 0.837) model. Additionally, our analysis concludes that the short duration and high-intensity rainfall, drainage density, lithology, and curvature are the major influencing factors for landslide occurrences in this case study area. This research provides an improved understanding of the mechanism of landslides caused by the typhoons for the adjoining watersheds nearby the reservoir. The preliminary understandings and approach could also be applied in similar geological and rainfall-triggered case study sites in the other parts of the world for risk mitigation.

The present study aims to assess the spatio-temporal growth of urban sub-centre of North 24 Parganas District of West Bengal, India during 1989–2016. Landsat TM and Landsat 8 OLI satellite data for 1989, 2006 and 2016 along with Shannon’s entropy model and Principal component analysis were used to assess the spatial dispersion and consistency of urbanization. A concentric zone analysis was also applied to assess the trends of urban growth within the urban sub-centre of the district. Land use/land cover change analysis reveals that the built up areas have increased drastically over the study periods. The transformation of prime agricultural land and other lands (mostly barren) into built up areas indicates the growth of sprawl within the urban sub-centre. The overall result shows that urban growth of the study area is not compact in nature and it is an evidence of concentration of sprawl growth over the urban sub-centre. The study estimates that urban expansion has occurred mainly in Barrackpore I, Barrackpore II and Rajarhat area of the district. The integrated methodology involving Shannon’s entropy model and Principal component analysis and use of remote sensing and GIS techniques has proved to be useful in analyzing urban expansion and trends of urban growth. The outcome of this study can best be utilized for effective urban planning and management. Keywords: Land use/land cover change, Shannon’s entropy model, Principal component analysis (PCA)

Floods have always been associated with widespread devastation and destruction since the emergence of human civilization. The intensity of this disaster has been increasing due to accelerated impact of human activities. Flood vulnerability is very diverse in nature and is multidimensional and a topic of vital significance. Hence, flood vulnerability assessment assumes greater significance since magnitude of destructions varies over space and time. The study makes a credible attempt to present a coherent review on the approaches and methodologies used for assessing flood and its vulnerability. A time frame of 1990–2018 was chosen for analyzing varied works carried out flood vulnerability and susceptibility assessment. Articles from Scopus and other reputed journals were used to review the works on flood assessments. Methods and approaches were examined by considering most-cited authors and keywords used in their works. The study revealed a gap existing between methods and approaches for evaluating flood vulnerability which can be incorporated by using high-resolution data along with using multidimensional approach for assessing vulnerability. Furthermore, this study calls for comprehensive flood assessment using artificial neural network, hydrodynamic models and geospatial techniques to provide a vivid visualization of flood susceptibility. The study may prove helpful in analyzing different components of vulnerability and guiding research gaps in methodology to be used for assessing flood vulnerability at spatial scales.

This paper attempts to outline the forced migration and consequent flee and displacement of the Rohingya Muslims in Northwestern Arakan State of Myanmar after several violent clashes and in...

Assessment of vulnerability to storm surge flood is not only significant for the survival of the exposed communities but also for adaptation. Defining vulnerability as the function of exposure, sensitivity and resilience capacity, we propose a composite vulnerability index (CVI) to assess village- level vulnerability to storm surge flood in the Sundarban Biosphere Reserve (SBR), India. The composite vulnerability index based analysis revealed that of the total villages (1063) located in SBR; nearly half of the villages are highly and very highly vulnerable with least capacity to cope with any kind of extreme event. The villages located in the southern parts and adjacent to coast were found more vulnerable to storm surge while those located at higher elevation in the central part showed low vulnerability. Some villages in northern part of the region were under high and very high vulnerability due to presence of low lying and waterlogged wetlands. The findings of the study may have implications for developing resilience capacity in response to storm surge flooding. Thus, the methodology can help the local policymakers in integrating local multi-hazards knowledge and in making information actionable for mitigation and adaptation of storm surge flood hazard.

It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer – Alternating Decision Tree – Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer – Deep Learning Neural Network – Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer – Multilayer Perceptron – Frequency Ratio (ICO-MLP-FR). The first stage of the manuscript consisted of the collection and processing of the geodatabase needed in the present study. The geodatabase comprises a number of 14 flood predictors and 132 known flood locations. The Correlation-based Feature Selection (CFS) method was used in order to assess the prediction capacity of the 14 predictors in terms of flood susceptibility estimation. The training and validation of the three ensemble models constitute the next stage of the scientific workflow. Several statistical metrics and ROC curve method were involved in the evaluation of the model's performance and accuracy. According to ROC curves all the models achieved high performances since their AUC had values above 0.89. ICO-DLNN-FR proved to be the most accurate model (AUC = 0.959). The outcomes of the study can be used to guide future flood risk management and sustainable land-use planning in the designated area.

UNLABELLED: The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00500-021-06012-9.