Every year, landslides create a major risk to the economy as well as human life in Himachal Pradesh, India. Frequent landslides in the state especially on the major highways cause serious problem for trade, connection, massive disruption to traffic and public safety. Hence, assessing the landslide susceptibility mapping (LSM) along the highway routes can significantly help in safeguarding the people and property. The primary objective of this research is to prepare LSM on the basis of eleven causative factors to identify very high, high, moderate, low and very low susceptibility. For this, Remote Sensing and Geographical Information System (GIS) based on Analytic Hierarchy Process (AHP) approach is practiced in this study to prepare the LSM along National Highway-5 (NH-5) from Solan to Shimla, in the state of Himachal Pradesh (H.P.), India. All these factors are further sub-classified and weightages are given as per AHP technique. A landslide susceptibility map is prepared from the combined weighted raster thematic maps of each factor based on the assigned values and rating. LSM was categorized into five categories such as: (1.18–2.00) very low, (2.00–2.45) low, (2.45–2.90) moderate, (2.90–3.40) high and (3.40–4.50) very high by natural break classifier in ArcGIS environment. The model demonstrates good predictive efficacy, evidenced by a ROC-AUC of 0.835, a Precision-Recall AUC of 0.943, and an F1-score of 0.923 at a threshold of 2.664. Furthermore, 97.92

The increase in the need for renewable energy sources has made the hybrid solar system, which can capture both electrical and thermal energy, a hot topic. The research presents the hybrid photovoltaic/thermal-thermoelectric (PV/T-TE) system, and the performance is enhanced by a Genetic Algorithm (GA)-based multi-parameter optimization framework. The GA is used to control important operating factors, such as the temperature of the PV surface, the flow rate of the cooling fluid, and the orientation of the panel, to maximize the energy and exergy efficiencies of the system. The optimal system had a consistent average energy efficiency of 75.44% and exergy efficiency over 93.8% during the seven days of operation, which is about 5-10% better than the conventional PV/T-TE configurations found in literature. Comprehensive daily and hourly performance evaluations support the claim that the thermal subsystem is responsible for the largest part of the total energy output, while the thermoelectric module just adds a little to it by taking waste heat and improving second-law efficiency. The sensitivity analysis points out that with the PV surface temperature rising above 70 degrees C, the photovoltaic efficiency drops by more than half; yet, the GA-based optimization permits the temperature to be kept at an optimal level, resulting in steady system performance. The findings prove that GA-based optimization offers a powerful tool for handling the nonlinear interactions in hybrid PV/T-TE systems, and it also indicates the promising future of such optimized systems for eco-friendly energy. Next, the researchers will carry out experiments to validate the findings, develop real-time control strategies, and conduct integrated techno-economic assessments.

Solar thermal technology holds significant potential for widespread utilization in the drying of agricultural and industrial commodities, effectively addressing challenges related to storage and transportation. This research seeks to evaluate the thermo-electrical performance of a forced-air convection-based PVT air-collector for drying tomatoes. Simulations were conducted in Ghaziabad City, India, considering local atmospheric conditions viz. solar irradiance intensity, ambient temperature, and the relative humidity of four different days from 7th–10th August 2023. The proposed dryer system comprises a PVT air-collector designed to provide both thermal and enhanced electrical energy, alongside a drying chamber, heat recovery system, and DC fan. Results indicate average thermal, electrical, and overall efficiencies of 36.04

Flood vulnerability mapping has significantly progressed with the advent of Machine Learning (ML), bringing greater certainty to predictions. However, conventional supervised ML techniques may not be feasible in regions where recorded flood inventory data is scarce. This study introduces a novel deep learning approach using a Convolutional Neural Network (CNN)-led Autoencoder to assess flood vulnerability under such conditions. The methodology utilizes eleven causative factors, represented as geospatial layers, to characterize the regional environment. These layers are processed using CNN Autoencoder and K-means clustering to produce a flood risk zonation map for the upper and middle basins of the Damodar River. The autoencoder’s reconstruction performance is evaluated using metrics Mean Squared Error (MSE), precision, recall, and accuracy apart from cluster-based indices to evaluate its classification ability. The resulting map shows that 92% of the study area is safe, while less than 8% faces moderate to very high flood risk, aligning with historical patterns and validation analysis. The study highlights the strong impact of Drainage Density on model outcomes, while certain factors like Aspect introduce noise. These findings provide valuable insights into flood vulnerability, even in data-scarce regions, aiding proactive mitigation strategies for future flood events.

In the present work, a passive solar dryer with mixed-mode operation and a north wall reflector inside the drying chamber has been developed and tested for drying tomato slices. Three trays inside the drying chamber were used to dry the 5 kg sliced tomatoes under clear sky conditions during winter. The evaluated performance of mixed-mode solar dryer (MMSD) with and without reflector and with open sun drying processes were compared. It is found that the MMSD with reflector is more effective followed by MMSD without reflector and open sun drying. The initial moisture content of sliced tomatoes was 94.5 %, reduced to 1 % using a north wall reflector at 24 h, 2.4 % without a reflector at 32 h, and 2.7 % at open sun drying. The solar dryer attained overall drying efficiencies with and without a north wall reflector were 22.07 %, and 15.38 % respectively. The findings indicate that using MMSD with the north wall reflector improved drying parameters, including moisture content, moisture ratio, and drying rates, compared to MMSD without the north wall reflector and open sun drying.

The most fundamental collectors for water heating are flat plate solar collectors (FPSCs). They are inexpensive, simple to build, and need little maintenance. Conventional FPSCs have relatively low efficiency. More study is needed to increase the efficiency of FPSC by using novel designs. To enhance thermal performance, various strategies have been implemented through design modifications of flow tubes. In this study, dual spiral-shaped flow tubes were constructed instead of the usual FPSCs number of riser tubes and headers and tested with three different mass flow rates (20.9, 29.9, and 42.1 l/h). The optimum instantaneous efficiency achieved is 70.8% at a flow rate of 42.1 l/h, representing a 13.7% improvement in efficiency compared to the conventional collector and lowering the mass flow rate results in an enhanced exergy efficiency of 3.51% at 20.9 l/h. The modified collector achieves its highest instantaneous efficiency when the outlet water temperature is measured at 53.4 degrees C. The modified collector boosts heat transfers by enlarging the water's surface contact area and prolonging its flow time, resulting in greater heat absorption and significantly improved efficiency. When all other parameters are kept the same as in a conventional design, highly encouraging results in a modified collector have been recorded.

Solar energy is the most feasible alternative to conventional sources for sustainability. In the present study, a novel mixed-mode solar dryer (MMSD) utilizing the north wall reflector has been developed in Chhattisgarh Swami Vivekanand Technical University, Bhilai, Chhattisgarh (21 degrees 27 N, 81 degrees 43 E) for the drying of vegetables. In this experiment, 5 kg of sliced tomatoes have been tested for the performance evaluation of the dryer. Three trays were positioned within the drying zone and uniformly loaded with tomato slices. Three distinct mass flow rates (MFRs) of 0.0456 kg/s, 0.0570 kg/s, and 0.0684 kg/s were evaluated for the MMSD, both with and without the north wall reflector. An analysis was conducted to compare the performance characteristics of both setups at all three MFRs, including open sun drying. Using the north wall reflector reduced the moisture content from 94.8% to 1.48% within 16 hours. Without the north wall reflector, it took 22 hours to reach a moisture level of 2.3%. However, the process of open-sun drying takes 38 hours to reach a moisture content of 2.7%. The MMSD with a reflector reduced the drying time by 33.3% compared to the MMSD without a reflector and by 60% compared to open-sun drying.

Geospatial technology (GT) has played a crucial role in identification of groundwater potential zones (GWPZ). Weighted overlay analysis (WOA) is a multicriterion study for the GWPZ under the umbrella of GT wherein investigation was carried out with multifaceted things for determining certain themes with the aid of assigning rank to the respective features class and then assign weightage to the respective parameters depending upon the weightage of the theme on the objective. For this purpose, criteria for the analysis were defined, and each parameter was assigned weightage based on its importance. In the present study, weighted overlay model in GIS environment (ArcGIS software) has been utilized to identify and demarcate the suitability for groundwater recharge zones in Kadiri basin of Ananthapuramu district, Andhra Pradesh, which was explored further for suitable recharge structures. Integration of various thematic layers was done for developing groundwater potential zones map of the study area which has four categories, i.e. poor, average, good and excellent GWPZ, respectively. Multiple thematic layers of influencing parameters were prepared and assigned features class rank as per the importance in the selection of recharge sites. Using this suitability modelling, suitable areas were identified wherein the classes with higher values indicate the most favourable zones for natural recharge in GIS platform and generated a composite map showing proposed locations for suitable groundwater recharge structures like check dams, percolation tanks, subsurface dykes and gabion structures.

Landslides are the nation's hidden disaster, significantly increasing economic loss and social disruption. Unfortunately, limited information is available about the depth and extent of landslides. Therefore, in order to identify landslide-prone zones in advance, a well-planned landslide susceptibility mapping (LSM) approach is needed. The present study evaluates the efficacy of an MCDA-based model (analytical hierarchy process (AHP)) and determines the most accurate approach for detecting landslide-prone zones in one part of Darjeeling, India. LSM is prepared using remote sensing thematic layers such as slope, rainfall earthquake, lineament density, drainage density, geology, geomorphology, aspect, land use and land cover (LULC), and soil. The result obtained is classified into four classes, i.e., very high (11.68%), high (26.18%), moderate (48.87%), and low (13.27%) landslide susceptibility. It is observed that an entire 37.86% of the area is in a high to very high susceptibility zone. The efficiency of the LSM was validated with the help of the receiver operating characteristics (ROC) curve, which demonstrate an accuracy of 96.8%, and the success rate curve showed an accuracy of 81.3%, both of which are very satisfactory results. Thus, the proposed framework will help natural disaster experts to reduce land vulnerability, as well as aid in future development.

The impact of the novel coronavirus disease (COVID-19) continues unabated. Still, it seems that apart from contact and respiratory transmission, the design and development pattern of an area does echoes to be a contributing factor in virus spreadability. The present study considers land use and transportation system parameters under TOD mode of 16 BRT station provinces in Bhopal, India, and COVID-19 cases data were collected from April 2020 to August 2020. Further, the Pearson correlation and mediational analysis were employed to determine the relationship between TODness and COVID-19 spread cases. The bootstrapping method was used to evaluate the mediation effect and describe why and under what conditions they are related. The study shows that TODness and COVID-19 spread cases are positively correlated. The results show a considerable correlation at (p < 0.05) is 0.405 of the dispersed along with TODness of an area in the analysed 16 BRT station areas. In particular, dispersed demonstrated a high-level correlation of 0.681 with TOD areas, whereas a moderate correlation of 0.322 with non-TOD areas was mediated by diversity and the number of available transit service indicators. Diversity and availability of high-quality transit services effectively spread the virus, whereas population density and public transport mediation effects are insignificant. Outcomes from this study may help government authorities and policymakers devise a strategy and adopt preventive measures in subsequent waves of the pandemic.

Evolutionary algorithms (EAs) are proficient in solving the controlled, nonlinear multimodal, non-convex problems that limit the use of deterministic approaches. The competencies of EA have been applied in solving various environmental and water resources problems. In this study, the storm water management model (SWMM) was set up to authenticate the capability of the model for simulating catchment response in the upper Damodar River basin. Auto-calibration and validation of SWMM were done for the years 2002-2011 at a daily scale using three EAs: genetic algorithms (GAs), particle swarm optimisation (PSO) and shuffled frog leaping algorithm (SFLA). Statistical parameters like Nash-Sutcliffe effectiveness (NSE), percent bias (PBIAS) and root-mean-squared error-observations standard deviation ratio (RSR) were used to analyse the efficacy of the results. NSE and PBIAS values obtained from GA were superior, with the recorded flow with NSE and PBIAS ranging between 0.63 and 0.69 and between 1.12 and 9.81, respectively, for five discharge locations. The value of RSR was approximately 0 indicating the sensibly exceptional performance of the model. The results obtained from SFLA were robust and superior to PSO. Our results showed the prospective use and blending of the hydrodynamic model with EA would aid the decision-makers in analysing the vulnerability in river watersheds.

Groundwater is getting contamination rapidly due to various anthropogenic activities and geogenic sources. In this direction, assessment of water quality analysis is the basic requirement for nurturing human being and its evolution. Water Quality Index (WQI) parameter have been widely used in determining water quality globally. The study aims to provide the suitability of groundwater in the specified region using polynomial approximation method for drinking and irrigation purposes along with the computation of WQI using conventional method. Weierstrass's polynomial approximation theorem along with longitudinal and latitudinal values has been used to evaluate the polynomial regarding various physico-chemical parameters. To validate the obtained results from the present approach, groundwater water quality data collected and analyzed from the Pindrawan tank area in Raipur district, Chhattisgarh, India have been used. The result obtained i.e., the Intermediate value of the parameters obtained correctly from the mathematical modeling with an average error of 7%. This polynomial approximation method can also be used as the substitute of inverse modeling to determine the location of the source in two-dimension system. The approach output can be beneficial to administrators in making decisions on groundwater quality and gaining insight into the tradeoff between system benefit and environmental requirement.

Urban surface runoff management via best management practices (BMP) and low impact development (LID) has earned significant recognition owing to positive environmental and ecological impacts. However, due to the complexity of the parameters involved, the estimation of LID efficiency in attenuating the urban surface runoff at the watershed scale is challenging. A planning analysis of employing Green Roofs and Infiltration Trenches as BMPs/LIDs practices for urban surface runoff control is presented in this study. A multi-objective optimization decision-making framework is established by coupling SWMM (Storm Water Management Model) with NSGA-II models to check the performance of BMPs/LIDs concerning the cost-benefit analysis of LID at the watershed scale. Two urbanized areas belonging to Central Delhi in India were used as case studies. The results showed that the SWMM model is useful in simulating optimization problems for managing urban surface runoff. The optimum scenarios efficiently minimized the urban runoff volume while maintaining the BMPs/LIDs implementation costs and size. With BMPs/LIDs implementation, the reduction in runoff volume increases as expenses increase initially; however, there is no noticeable reduction in flood volume after a certain threshold. Contrasted with the haphazard arrangement of BMPs/LIDs, the proposed approach demonstrates 22%-24% runoff reductions for the same expenditures in watershed 1 and 23%-26% in watershed 2. The result of the study provides insights into planning and management of the urban surface runoff control with LID practices. The proposed framework assists the hydrologists in optimum selection and placements of BMPs/LIDs practices to acquire the most extreme ecological advantages with the least expenses.

Wetlands in urban ecosystems provide significant environmental benefits. In the present study, the concept of urban constructed wetland development is studied from the viewpoint of urban planning with dynamic water level orifice setting controller. A two-step modelling procedure is carried out: (1) development of a hybrid model, by coupling a well-established two-dimensional hydrodynamic model (International River Interface Cooperative, iRIC) with a one-dimensional physically-based, distributed-parameter model (Storm Water Management Model, SWMM), to compute and map flood scenarios and to identify the flood-prone areas; and (2) use of SWMM to simulate the water inflow to the proposed constructed wetland, which acts as a cushion for storing excess flood water. The proposed methodology is implemented on the Jahangirpuri drain catchment located in Delhi, India. Results show that the hybrid model is effective, and the simulations are observed to be in good agreement with the recorded data, which assist in detecting the flood-prone areas. Further, an estimation of the impact of the proposed constructed wetland on catchment hydrology indicates an overall reduction of 23% in flooding adjacent to the channel with a significant reduction in backflow as well as water depth in the drain. The flapgate at the outlet of the wetland helps in maintaining the desired water depth in the wetland. The outcomes of this study will assist the hydrologists and administrators in urban stormwater management and planning to mitigate the impact of floods in urban watersheds.

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

Climate change and urbanization are significantly magnifying flood hazard, leading to a greater vulnerability of urban concentrations. This paper investigates the impact of climate change on urban flooding using future projected rainfall data and a calibrated hydraulic model. Two urban watersheds in Delhi, India (the Qudesia Nallah catchment and the Jahangirpuri drain catchment) are considered to evaluate the climate change impact on urban flooding. Regional climate models (RCMs) are used to project future precipitation, which is then utilized by the hydraulic model to evaluate the impact on flooding. Climate data from three RCMs extracted from the Coordinated Regional Climate Downscaling Experiment (CORDEX) are used to study the impact of climate change for historical (1990–2016) and future scenario (Representative Concentration Pathway (RCP) 4.5, 2021–2100). The rainfall projections are fed as 2-, 5-, 10-, and 20-year return periods to a calibrated hydrodynamic Storm Water Management Model (SWMM). The results show that the flooded nodes vary between 2–6 and 12–43, respectively, in the Qudesia Nallah catchment and the Jahangirpuri drain catchment under present conditions but increase from 11 to 51 and 42 to 91, respectively, for future climate conditions. The results suggest that the risk of occurrence of flooding, duration, and frequency in the two study areas will increase in the future when compared to those under the present conditions. The results also indicate that the damage induced by the 20-year return period rainfall at the present time will likely be caused just by the 2-year return period in the future. This is due to the greater likelihood of rainfall extremes in the region. The potential flooding sites identified in this study will provide the urban municipalities with substantive information to perform ameliorative strategies.

Flooding has caused immense damage to the people as well as to the property. Flooding in urban areas mostly occurs due to increased urbanization, low rate of infiltration and poor infrastructure for stormwater drainage network. Stormwater Management Model (SWMM) is found to be very dynamic hydrology-hydraulic water quality simulation model for modeling of the urban stormwater drainage network. In the present study, PCSWMM model is used for modeling the stormwater drainage network for the southern part of Delhi, the capital city of India. PCSWMM is developed by Computational Hydraulics International (CHI), Canada. PCSWMM uses the same SWMM engine for the modeling work; the only advantage is that it is GIS compatible software which makes this model more efficient. The model required following input information for simulation, i.e., land-use for calculating impervious and previous area, soil type, 15-minute interval precipitation data, temperature, humidity, and three-dimension cross-sectional geometry of the existing drainage network. A field survey was carried out for data collection, and in the process, it was found that most of the storm-water drains are choked, have improper flow gradient 370or damaged. All the collected field details of the storm-water drains were incorporated in ArcMap 10.1 and then imported in PCSWMM to develop a hydrology-hydraulic model for surface runoff. The simulated results of the model were further calibrated and validated with the available flooding locations data obtained from the Delhi Traffic Police Department. The simulated results were in close agreement with the observed flooding locations. Thus PCSWMM model can be applied to any urban/rural areas for designing stormwater drains or drainage network.