Retraction Note: Weather indicators and improving air quality in association with COVID-19 pandemic in India.

[This retracts the article DOI: 10.1007/s00500-021-06012-9.].

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research.

Assessment of suitable habitat of mangrove species for prioritizing restoration in coastal ecosystem of Sundarban Biosphere Reserve, India.

Mangrove forests being the abode of diverse fauna and flora are vital for healthy coastal ecosystems. These forests act as a carbon sequester and protection shield against floods, storms, and cyclones. The mangroves of the Sundarban Biosphere Reserve (SBR), being one of the most dynamic and productive ecosystems in the world are in constant degradation.

Comparing Delphi-fuzzy AHP and fuzzy logic membership in soil fertility assessment: a study of an active Ganga Delta in Sundarban Biosphere Reserve, India.

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 (PO, KO, Na, Zn, B) in the active Ganga deltaic region of Sundarban Biosphere Reserve, India. Soil samples have been collected from 30 soil grids, and 0-15 cm soil depth was preferred for fertility analysis because most essential soil chemical and nutrient elements affecting soil fertility are concentrated in this depth range. We have used the fuzzy-AHP-Delphi (FAHP) and fuzzy logic-Delphi (FL) methods to determine the soil fertility zone.

Wetland health, water quality, and resident perceptions of declining ecosystem services: a case study of Mount Abu, Rajasthan, India.

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.

Flood susceptibility evaluation through deep learning optimizer ensembles and GIS techniques.

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).

Weather indicators and improving air quality in association with COVID-19 pandemic in India.

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 (PM, PM, CO, NO, SO, NH, and O) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown.

Plastic waste footprint in the context of COVID-19: Reduction challenges and policy recommendations towards sustainable development goals.

The sudden surge in demand to use plastic products due to COVID-19 pandemic has increased plastic pollution. It has resulted into degradation of a broad range of habitats and ecosystems by destroying natural functions, water quality, and environmental sustainability. However, the government agencies, scientific communities, and the public, have started to give attention to this issue.

Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility.

The objective of this study is to assess the gully head-cut erosion susceptibility and identify gully erosion prone areas in the Meimand watershed, Iran. In recent years, this study area has been greatly influenced by several head-cut gullies due to unusual climatic factors and human induced activity. The present study is therefore intended to address this issue by developing head-cut gully erosion prediction maps using boosting ensemble machine learning algorithms, namely Boosted Tree (BT), Boosted Generalized Linear Models (BGLM), Boosted Regression Tree (BRT), Extreme Gradient Boosting (XGB), and Deep Boost (DB).

A district-level susceptibility and vulnerability assessment of the COVID-19 pandemic's footprint in India.

In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next.

A systematic review on high conservation value assessment (HCVs): Challenges and framework for future research on conservation strategy.

Various scholars and research institutions have attempted to assess High Conservation Values (HCVs) using different methodologies and approaches. Various countries have developed toolkits to determine High Conservation Value Areas (HCVAs) according to their needs and conservation strategies but there is no global agreement on them. The present study attempts to review research papers and assessment reports from 1999 until 2018 on approaches and methodologies used for HCVs all over the world and provide a review into HCV research systematically, with due consideration to the linkages between Biodiversity, Ecosystem Services and Socio-Economic-Cultural values.

Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model.

This study aimed to model deforestation susceptibility in forest ecosystem of Rudraprayag district, India. For this purpose, site-specific physical (slope angle, slope aspect, altitude, annual average rainfall, soil texture, soil depth), and anthropogenic (population distribution, distance from road, distance from settlement, proximity to agricultural land) deforestation conditioning factors were chosen. Landsat TM and OLI images for 1990 and 2015 were utilized to evaluate the changes in forest cover.

Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan.

Landslides represent a part of the cascade of geological hazards in a wide range of geo-environments. In this study, we aim to investigate and compare the performance of two state-of-the-art machine learning models, i.e.

Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: A study on Kolkata urban agglomeration, India.

This research paper analyzed urban spatial pattern and trend of urban growth in Kolkata urban agglomeration, India using urban sprawl matrix during 1990-2000 & 2000-2015. Seven urban classes viz. urban primary core, urban secondary core, sub urban fringe, scatter settlement, urban open space, non-urban area and water body were chosen for analyzing the magnitude and direction of urban expansion.