Skilled birth attendance and institutional delivery have been advocated for reducing maternal, neonatal mortality and infant mortality (NMR and IMR). This paper examines the role of place of delivery with respect to neo-natal and infant mortality in India using four rounds of the Indian National Family Health Survey conducted in 2015–2016. The place of birth has been categorized as “at home” or “public and private institution.” The role of place of delivery on neo-natal and infant mortality was examined by using multivariate hazard regression models adjusted for clus-tering and relevant maternal, socio-economic, pregnancy and new-born characteristics. There were 141,028 deliveries recorded in public institutions and 54,338 in private institutions. The esti-mated neonatal mortality rate in public and private institutions during this period was 27 and 26 per 1000 live births respectively. The study shows that when the mother delivers child at home, the chances of neonatal mortality risks are higher than the mortality among children born at the health facility centers. Regression analysis also indicates that a professionally qualified provider′s antenatal treatment and assistance greatly decreases the risks of neonatal mortality. The results of the study illustrate the importance of the provision of institutional facilities and proper pregnancy in the prevention of neonatal and infant deaths. To improve the quality of care during and imme-diately after delivery in health facilities, particularly in public hospitals and in rural areas, accel-erated strengthening is required.

Accurate knowledge of the recharging rate is essential for several groundwater-related studies and projects mainly in the water scarcity regions. In this study, a comparison between different methods of soft computing-based models was obtained in order to evaluate and select the most suitable and accurate method for predicting the recharging rate of groundwater, as the natural recharging rate of the groundwater is important in efficient groundwater resource management and aquifer recharge. Experimental data have been used to investigate the improved performance of Gaussian process (GP), M5P and random forest (RF)-based regression method and evaluate the potential of these techniques in the prediction of natural recharging rate. The study also compares the prediction of recharging rate to empirical (Kostiakov model, multilinear regression, multi-nonlinear regression) equations. The RF method was selected for the recharging rate prediction and was compared with the M5P tree, GP and also empirical models. While GP, M5P tree and empirical models provide good quality of prediction performance, RF model showed superiority among them with coefficient of correlation (R) values as 0.98 and 0.91 for training and testing, respectively. Out of 106 observations collected from laboratory experiments, 73 were used for developing different models, whereas rest 33 observations were used for the assessment of the models’ performance. Sensitivity analysis recommends that time parameter (t) is the main influencing parameter, which is crucial for the prediction of the recharging rate. RF-based model is suitable for accurate prediction of recharging rate of groundwater.

Water scarcity is one of the worlds, fastest growing epidemics. Therefore, to combat it or mitigate the risks one must first understand how water is being consumed. This study focuses on the analysis of domestic water consumption in reference to how much of it is being consumed. Additionally, the study aims to propose an applicable and consistent method to forecast urban water consumption by using the soft computing techniques. The investigation highlights the hourly, daily and monthly water consumption levels as well as the relationship between climate change and water demand using gene expression programming (GEP). The study results of the study are relatively promising as it demonstrates that GEP can predict water consumption incorporating seasonal changes of wet and dry periods.

The purpose of this paper is to use the application of the multilinear lag cascade model as a contaminant transport model through river networks. Monocacy River and Antietam Creek data, which were collected by USGS with different reach lengths and discharges conditions, have been used in the current study. It was found that multilinear discrete lag-cascade (MDLC) model is capable of reconstructing contaminant breakthrough curves. A complete study was performed to estimate the reach length for use in the accurate simulation, and it was concluded that by assuming a uniform flow through the reach, the length unit should be obtained by applying Pe = 12. Moreover, by using temporal moment matching, explicit relationships for MDLC model parameters (k, n, and τ) and based on conventional advection-dispersion equation (ADE) parameters (D, u, x) were extracted. MDLC parameters of the field breakthrough curves were extracted, and it was found that the increase of Pe number caused to increase of delay time and the number of cascades. However, the residence time was obtained to be fixed. Additionally, by assuming the dispersivity parameter (D/u) is constant, the changes in the MDLC parameters were investigated by velocity variation, and new relationships were proposed to estimate its parameters under different hydraulic conditions. Using presented equations provided in this study for residence time (k), cascade number (n), and delay time (τ), the sensitivity analysis was performed, and it was found that the parameters of velocity (u), dispersion coefficient (D), and velocity (u) have the most important effect in calculation of them, respectively.

The infiltration process plays a key role in designing groundwater recharge, irrigation, and drainage systems, and contamination evaluation is controlled by numerous factors, among which soil physical properties and land use & land cover (LULC) are the prime factors. A comprehensive understanding of the spatial water infiltration characteristics over the soil which is site-specific and more complex due to non-uniformity could enhance the agriculture water use efficiency and mitigate water-related issues. The present study deals with the measurement of field infiltration characteristics using a mini disc infiltrometer in all 24 blocks of Gaya districts, Bihar, which covers a wide spectrum of soil types. Results showed that the average cumulative infiltration rate (IR) for the study area varies between 0.38 and 2.20 cm/min with an average rate of 1.16 cm/min. The initial IR among all blocks was found to be high but decreased gradually with each successive reading. Moreover, the land use under forest cumulative IR was more than the cumulative IR for urban and grassland. Eight blocks (33.3%) have an IR more than the average infiltration of the area which is good for storing the water in the aquifer and suggested constructing a recharge structure. Further investigation revealed a small IR in the inundated area, because of the maximum soil water table. The ready-to-use map showing the IR for the district is prepared which could be used by any decision-taking during the high or low rainfall, understanding the hydrological process, development of any reference guide for farmers for increasing the agriculture productivity and soil-water management.