Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool for the assessment of satisfied degree of crop water requirements (CWR). Crop combination, water availability for agricultural production, water accessibility, and other indices were calculated considering the DEM, hydrological and climatic data, and crop properties for measuring the agricultural water requirement and satisfied degree in Bansloi River basin using the CropWRA model. Advanced machine learning model random forest was used to calculate the soil moisture considering the atmospheric variable, Landsat indices, and energy balance components for calculating the crop water satisfied degree and water requirement. The average crop water demand is 1.92 m, and it ranges from 1.58 to 2.26 m. The demand of crop water is more in the western part of the basin than the eastern part. The CropWSD (crop water satisfied degree) ranges from 17 to 116% due to variation in topography, river system, crop combination, land use, water uses, etc. The average crop water satisfied degree is 59%. About 71% of the total area is under 40% to 60% CropWSD level. CropWRA model can be applied for the sustainable water resource management, irrigation infrastructure development, and use of other modern technologies.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi.

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

Habitat vulnerability in slum areas of India - What we learnt from COVID-19?

UN-Habitat identified the present COVID-19 pandemic as 'city-centric'. In India, more than 50% of the total cases were documented in megacities and million-plus cities. The slums of cities are the most vulnerable due to its unhygienic environment and high population density that requires an urgent implementation of public healthcare measures. This study aims to examine habitat vulnerability in slum areas to COVID-19 in India using principal component analysis and Fuzzy AHP based technique to develop slum vulnerability index to COVID-19 (SVIcovid-19). Four slum vulnerability groups (i.e. principal components) were retained with eigen-values greater than 1 based on Kaiser criterion - poor slum household status; lack of social distance maintenance; high concentrations of slum population and towns and mobility of the households. This study also mapped composite SVIcovid-19 on the basis of PCA and Fuzzy AHP method at the state level for a better understanding of spatial variations. The result shows that slums located in the eastern and central parts of India (particularly Uttar Pradesh, Bihar, Jharkhand, Odisha, West Bengal) were more vulnerable to COVID-19 transmission due to lack of availability as well as accessibility to the basic services and amenities to slum dwellers. Thus, the findings of the study may not only help to understand the habitat vulnerability in slum areas to COVID-19 but it will also teach a lesson to implement effective policies for enhancing the quality of slum households (HHs) and to reduce the health risk from any infectious disease in future.

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms.

Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may-support landslide prediction and management.

Regional scenario of air pollution in lockdown due to COVID-19 pandemic: Evidence from major urban agglomerations of India.

Air pollution in India during COVID-19 lockdown, which imposed on 25th March to 31st May 2020, has brought a significant improvement in air quality. The present paper mainly focuses on the scenario of air pollution level (PM2.5, PM10, SO2, NO2 and O3) across 57 urban agglomerations (UAs) of India during lockdown. For analysis, India has been divided into six regions - Northern, Western, Central, Southern, Eastern and North-Eastern. Various spatial statistical modelling with composite air quality index (CAQI) have been utilised to examine the spatial pattern of air pollution level. The result shows that concentration of all air pollutants decreased significantly (except O3) during lockdown. The maximum decrease is the concentration of NO2 (40%) followed by PM2.5 (32%), PM10 (24%) and SO2 (18%). Among 57 UA's, only five - Panipat (1.00), Ghaziabad (0.76), Delhi (0.74), Gurugram (0.72) and Varanasi (0.71) had least improvement in air pollution level considering entire lockdown period. The outcome of this study has an immense scope to understand the regional scenario of air pollution level and to implement effective strategies for environmental sustainability.

Examining the impact of lockdown (due to COVID-19) on ambient aerosols (PM2.5): A study on Indo-Gangetic Plain (IGP) Cities, India.

The outbreak of COVID-19 pandemic has impacted all the aspects of environment. The numbers of COVID-19 cases and deaths are increasing across the globe. In many countries lockdown has been imposed at local, regional as well as national level to combat with this global pandemic that caused the improvement of air quality. In India also lockdown was imposed on 25th March, 2020 and it was further extended in different phases. The lockdown due to outbreak of COVID-19 pandemic has showed substantial reduction of PM2.5 concentrations across the cities of India. The present study aims to assess concentration of PM2.5 across 12 cities located in different spatial segments Indo-Gangetic Plain (IGP). The result showed that there was substantial decrease of PM2.5 concentrations across the cities located in IGP after implementation of lockdown. Before 30 days of lockdown, average PM2.5 across cities was 65.77 µg/m3 that reached to 42.72 µg/m3 during lockdown periods (decreased by 35%). Maximum decrease of PM2.5 concentrations has been documented in Lower Gangetic Plain (LGP) cities (57%) followed by Middle Gangetic Plain (MGP) cities (34%) and Upper Gangetic Plain (UGP) cities (27%) respectively. Among all the cities of IGP, maximum decrease of PM2.5 concentrations was recorded in Kolkata (64%) (LGP) followed by Muzaffarpur (53%) (MGP), Asansol (51%) (LGP), Patna (43%) (MGP) and Varanasi (33%) (MGP).Therefore, this study has an immense potentiality to understand the impact of lockdown on a physical region (Ganga River Basin) and it may be also helpful for planners and policy makers to implement effective measures at regional level to control air pollution.

Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method.

The present research examines the landslide susceptibility in Rudraprayag district of Uttarakhand, India using the conditional probability (CP) statistical technique, the boost regression tree (BRT) machine learning algorithm, and the CP-BRT ensemble approach to improve the accuracy of the BRT model. Using the four fold of data, the models' outcomes were cross-checked. The locations of existing landslides were detected by general field surveys and relevant records. 220 previous landslide locations were obtained, presented as an inventory map, and divided into four folds to calibrate and authenticate the models. For modelling the landslide susceptibility, twelve LCFs (landslide conditioning factors) were used. Two statistical methods, i.e. the mean absolute error (MAE) and the root mean square error (RMSE), one statistical test, i.e. the Freidman rank test, as well as the receiver operating characteristic (ROC), efficiency and precision were used for authenticating the produced landslide models. The results of the accuracy measures revealed that all models have good potential to recognize the landslide susceptibility in the Garhwal Himalayan region. Among these models, the ensemble model achieved a higher accuracy (precision: 0.829, efficiency: 0.833, AUC: 89.460, RMSE: 0.069 and MAE: 0.141) than the individual models. According to the outcome of the ensemble simulations, the BRT model's predictive accuracy was enhanced by integrating it with the statistical model (CP). The study showed that the areas of fallow land, plantation fields, and roadsides with elevations of more than 1500 m. with steep slopes of 24° to 87° and eroding hills are highly susceptible to landslides. The findings of this work could help in minimizing the landslides' risk in the Western Himalaya and its adjoining areas with similar landscapes and geological characteristics.

Inadequate awareness of cervical cancer and its prevention among young women of Delhi, India: public health policy implications.

BACKGROUND: The increasing burden of cancer is a cause of concern worldwide including in India. Cervical cancer is amongst the most common cancers among women associated with high morbidity and mortality. Younger women are at risk of acquiring human papilloma virus (HPV) infection that can lead to cervical cancer later in life. The present study is an attempt to assess awareness about cervical cancer, its prevention and HPV among young women so that future policies can be designed accordingly. METHODOLOGY: This was a cross-sectional study conducted among college-going women students of Delhi. Data was collected using a pre-designed, pretested semi-structured tool followed by descriptive statistical analysis. RESULTS: Although 83% women students had heard of cervical cancer, the signs and symptoms were known to less than half (41.9%) of the students. HPV vaccine availability was known to 56.0% of the students, but very few students were vaccinated (15.0%). Similar disparity was also found in screening knowledge and practices. CONCLUSION: With poor knowledge about risk factors, and preventive strategies among young women, this study highlights the need for health education programmes related to cervical cancer targeting young women. As most of the risk factors of cervical cancer are modifiable, awareness generation at a young age could bring about a paradigm shift in incidence and the mortality associated with it.

Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, India.

Rapid population growth and its corresponding effects like the expansion of human settlement, increasing agricultural land, and industry lead to the loss of forest area in most parts of the world especially in such highly populated nations like India. Forest canopy density (FCD) is a useful measure to assess the forest cover change in its own as numerous works of forest change have been done using only FCD with the help of remote sensing and GIS. The coupling of binary logistic regression (BLR), random forest (RF), ensemble of rotational forest and reduced error pruning trees (RTF-REPTree) with FCD makes it more convenient to find out the deforestation probability. Advanced vegetation index (AVI), bare soil index (BSI), shadow index (SI), and scaled vegetation density (VD) derived from Landsat imageries are the main input parameters to identify the FCD. After preparing the FCDs of 1990, 2000, 2010 and 2017 the deforestation map of the study area was prepared and considered as dependent parameter for deforestation probability modelling. On the other hand, twelve deforestation determining factors were used to delineate the deforestation probability with the help of BLR, RF and RTF-REPTree models. These deforestation probability models were validated through area under curve (AUC), receiver operating characteristics (ROC), efficiency, true skill statistics (TSS) and Kappa co-efficient. The validation result shows that all the models like BLR (AUC = 0.874), RF (AUC = 0.886) and RTF-REPTree (AUC = 0.919) have good capability of assessing the deforestation probability but among them, RTF-REPTree has the highest accuracy level. The result also shows that low canopy density area i.e. not under the dense forest cover has increased by 9.26% from 1990 to 2017. Besides, nearly 30% of the forested land is under high to very high deforestation probable zone, which needs to be protected with immediate measures.

A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility.

Land subsidence (LS) is a significant problem that can cause loss of life, damage property, and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a major problem for sustainable development and management. The plain represents the changes occurring in 40% of the country. We introduce a novel-ensemble intelligence approach (called ANN-bagging) that uses bagging as a meta- or ensemble-classifier of an artificial neural network (ANN) to predict LS spatially on the Semnan Plain in Semnan Province, Iran. The ensemble model's goodness-of-fit (to training data) and prediction accuracy (of the validation data) are compared to benchmarks set by ANN-bagging. A total of 96 locations of LS and 12 LS conditioning factors (LSCFs) were collected. Each feature in the LS inventory map (LSIM) was randomly assigned to one of four groups or folds, each comprising 25% of cases. The novel ensemble model was trained using 75% (3 folds) and validated with the remaining 25% (1 fold) in a four-fold cross-validation (CV) system, which is used to control for the effects of the random selection of the training and validation datasets. LSCFs for LS prediction were selected using the information-gain ratio and multi-collinearity test methods. Factor significance was evaluated using a random forest (RF) model. Groundwater drawdown, land use and land cover, elevation, and lithology were the most important LSCFs. Using the k-fold CV approaches, twelve LS susceptibility maps (LSSMs) were prepared as each fold employed all three models (ANN-bagging, ANN, and bagging). The LS susceptibility mapping showed that between 5.7% and 12.6% of the plain had very high LS susceptibility. All three models produced LS susceptibility maps with acceptable prediction accuracies and goodness-of-fits, but the best maps were produced by the ANN-bagging ensemble method. Overall, LS risk was highest in agricultural areas with high groundwater drawdown in the flat lowlands on quaternary sediments (Qcf). Groundwater extraction rates should be monitored and potentially limited in regions of severe or high LS susceptibility. This investigation details a novel methodology that can help environmental planners and policy makers to mitigate LS to help achieve sustainability.

Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India.

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic(AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions.

Determinants of online health information-seeking behavior: A cross-sectional survey among residents of an urban settlement in Delhi.

BACKGROUND: In health care, the rapid proliferation of health information on the internet has resulted in more patients turning to the digital media as their first source of health information and acquiring knowledge. The present study was conducted to assess use of the digital medium as a medical information resource in health-related states and to determine their experience and perceptions about the quality and reliability of the information available among the participants. METHODOLOGY: The study was done in an urban settlement of Delhi among adults who use any digital media. A sample of 321 were selected though convenient sampling. The information was collected through a semi-structured, self-administered, pre-tested questionnaire which contained questions on socio-demographic profile, internet usage and awareness about Digital India. Bivariate analysis was done to determine the association between various socio-demographic variables associated with internet usage for health information. RESULTS: In the present study, 88.2% (283/321) were using the internet for health information through digital media. This study found out that younger age group (18-30 years), literate and higher socioeconomic group (upper middle and above) population were more likely to access health information via digital media which was found out to be statistically significant. CONCLUSION: Access to health information through digitization can improve health literacy among the population and help in promoting a preventive aspect to health problems and disease. They can be the building blocks to build "Swasth Bharat (Healthy India)".

Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms.

Gully erosion is one of the most effective drivers of sediment removal and runoff from highland areas to valley floors and stable channels, where continued off-site effects of water erosion occur. Gully initiation and development is a natural process that greatly impacts natural resources, agricultural activities, and environmental quality as it promotes land and water degradation, ecosystem disruption, and intensification of hazards. In this research, an attempt is made to produce gully erosion susceptibility maps for the management of hazard-prone areas in the Pathro River Basin of India using four well-known machine learning models, namely, multivariate additive regression splines (MARS), flexible discriminant analysis (FDA), random forest (RF), and support vector machine (SVM). To support this effort, observations from 174 gully erosion sites were made using field surveys. Of the 174 observations, 70% were randomly split into a training data set to build susceptibility models and the remaining 30% were used to validate the newly built models. Twelve gully erosion conditioning factors were assessed to find the areas most susceptible to gully erosion. The predisposing factors were slope gradient, altitude, plan curvature, slope aspect, land use, slope length (LS), topographical wetness index (TWI), drainage density, soil type, distance from the river, distance from the lineament, and distance from the road. Finally, the results from the four applied models were validated with the help of ROC (Receiver Operating Characteristics) curves. The AUC value for the RF model was calculated to be 96.2%, whereas for those for the FDA, MARS, and SVM models were 84.2%, 91.4%, and 88.3%, respectively. The AUC results indicated that the random forest model had the highest prediction accuracy, followed by the MARS, SVM, and FDA models. However, it could be concluded that all the machine learning models performed well according to their prediction accuracy. The produced GESMs can be very useful for land managers and policy makers as they can be used to initiate remedial measures and erosion hazard mitigation in prioritized areas.

Use of information on pre-packaged foods among residents of an urban village of South Delhi, India.

Background: With rapid urbanization and hectic lifestyle, there is a growing demand of pre-packaged food items. 'Food label', present on most packaged food items provides information about the contents, their nutritive value and other information that can help the consumer to make an informed choice. Few studies in India have assessed the consumer's knowledge and practices related to information on a food label. Methods: We assessed the awareness, perceptions and practices related to the use of information on food labels among residents of an urbanized village of south Delhi. House-to-house visits were made and information gathered using a pre-designed, pre-tested, semi-structured questionnaire. Descriptive analysis was done and logistic regression performed to document the determinants of 'reading food label' by the study participants. Results: A total of 368 individuals were interviewed. The mean (SD) age of the participants was 29.1 (9.7) years. Around one-fourth (97/368; 26.4%) of all participants reported buying pre-packaged foods daily. A majority (222/ 368; 60%) of participants bought pre-packaged foods because they liked the taste, and also because they were easily available (153/368; 41.7%). A total of 64.1% (236/368) reported that they read food labels, but a majority checked only for the manufacture and expiry dates (203/236; 86%). Educational status, socioeconomic status and body mass index of the study participants were found to be significantly associated with reading of labels. Conclusions: The intention of promoting healthy food choices through the use of food labels is met inadequately at present. Awareness generation activities would be required to improve this behaviour.