49 research outputs found
Route alignment planning for a new highway between two cities using Geoinformatics techniques
No full textAn attempt has been made to delineate and identify the alignment of a new route between two important cities of north India, Haridwar & Roorkee using Geoinformatics techniques. Geo-engineering parameters like slope, aspect, geology, land use, drainage and soil along with some techno-economical parameters have been used for this purpose. Multi-criteria weight method has been applied. Five weighting methods (AHP - Analytical Hierarchy Process, Rank Sum, Rank Reciprocal, Rank Exponent and Ratio Estimation) were applied simultaneously to eliminate biasness in weight assignment to the input parameters. The results show that AHP method is the best and ratio estimation method is the second best method for identification of optimum route alignment. Few more parameters were used for final selection of optimum route viz., minimum construction cost; minimum number of bridges and culverts on that route; maximum number of settlement within 5 km buffers on both sides of route; maximum number of tourist locations like temples, waterfalls, springs etc. within 5 km buffer zone on both side of route. The proposed route between Roorkee and Haridwar towns is only 29.22 km long (includes a 17.10 km long part of the existing road), the new road required is 12.12 km, while the existing longer route between Roorkee and Haridwar is 33 km (instead of 29.22 km). By using multi-criteria weighted methods of route alignment, a length of approximately 3.78 km can be avoided. It was also observed that slope, land use and drainage parameters are more sensitive for route alignment
Assessing the Cooling Effect of Blue-Green Spaces: Implications for Urban Heat Island Mitigation
No full textThe Urban Heat Island (UHI) effect is a significant concern in today’s rapidly urbanising cities, with exacerbating heatwaves’ impact, urban livelihood, and environmental well-being. This study aims to assess the cooling effect of blue-green spaces in Bhubaneswar, India, and explore their implications for mitigating UHI effects. Satellite images were processed with Google Earth Engine (GEE) to produce information on the blue-green spaces’ land surface temperatures (LST). The Normalised Difference Vegetation Index (NDVI) and Modified Normalised Difference Water Index (MNDWI) were employed to quantify the presence and characteristics of these blue-green spaces. The findings revealed significant spatial variations in the LST, with higher temperatures observed in bare land and built-up areas and lower temperatures in proximity to the blue-green spaces. In addition, a correlation analysis indicated the strong influence of the built-up index (NDBI) on the LST, emphasising the impact of urbanisation on local climate dynamics. The analysis demonstrated the potential of blue-green spaces in reducing surface temperatures and mitigating UHI effects. Based on these results, strategic interventions were proposed, such as increasing the coverage of green spaces, optimising access to water bodies, and integrating water-sensitive design principles into urban planning to enhance the cooling effects and foster a more sustainable and resilient urban environment. This study highlighted the importance of leveraging remote sensing and GEE for urban UHI analyses. It provides valuable insights for policymakers and urban planners to prioritise nature-based solutions for heat mitigation in Bhubaneswar and other similar cities. Future research could delve deeper into a quantitative assessment of the cooling benefits of specific blue-green infrastructure interventions and explore their socio-economic impacts on urban communities
Performance investigations on modified vertical axis water turbine: Combination of lift and drag
No full textDesign of Double-F Metamaterial Structure for Enhancing Bandwidth of Patch Antenna with Negative μ and e
No full textLatitudinal Trend Analysis of Land Surface Temperature to Identify Urban Heat Pockets in Global Coastal Megacities
No full textRecent global warming has led to increased coastal disturbances through a significant transfer of heat between the land and the ocean surface. The polar regions show excessive temperature changes resulting in massive ice sheet melting. Mid-latitudinal storms pull heat away from the equator towards the poles; therefore, the global sea level is rising, making coastal cities the most vulnerable. In last few decades, rapid urbanization in big cities has drastically changed the land cover and land use due to deforestation, which has led to increased land surface temperatures (LSTs). This eventually leads to urban flooding due to oceanic storm surges frequently created by low pressure over the ocean during summer. This paper considered factors such as drastic unplanned urbanization to analyze coastal cities as the focal point of the generation of heat yielding the annihilation of the natural topography. Urban heat pockets (UHP) were studied for nine megacities, which were selected at an interval of 5° of latitudinal difference in the northern hemisphere (NH) since 70% of densely populated megacities are located in coastal regions. A comparative surface temperature analysis was effectively carried out with the same latitudinal reference for nine mid-sized cities using the derived LST data from Landsat 8. The results provide a comparative classification of surface temperature variations across the coastal cities over the NH. This study infers that the issues pertaining to growing urbanization are very important for analyzing the proportional impact caused by the settlement hierarchy and lays a robust foundation for advanced studies of global warming in coastal urban environments
Design of electricity tariff plans using gap statistic for K-means clustering based on consumers monthly electricity consumption data
No full text
Purpose
Electricity consumption around the world and in India is continuously increasing over the years. Presently, there is a huge diversity in electricity tariffs across states in India. This paper aims to focus on development of new tariff design method using K-means clustering and gap statistic.
Design/methodology/approach
Numbers of tariff plans are selected using gap-statistic for K-means clustering and regression analysis is used to deduce new tariffs from existing tariffs. The study has been carried on nearly 27,000 residential consumers from Sangli city, Maharashtra State, India.
Findings
These tariff plans are proposed with two objectives: first, possibility to shift consumer’s from existing to lower tariff plan for saving electricity and, second, to increase revenue by increasing tariff charges using Pay-by-Use policy.
Research limitations/implications
The study can be performed on hourly or daily data using automatic meter reading and to introduce Time of Use or demand based tariff.
Practical implications
The proposed study focuses on use of data mining techniques for tariff planning based on consumer’s electricity usage pattern. It will be helpful to detect abnormalities in consumption pattern as well as forecasting electricity usage.
Social implications
Consumers will be able to decide own monthly electricity consumption and related tariff leading to electricity savings, as well as high electricity consumption consumers have to pay more tariff charges for extra electricity usage.
Originality/value
To remove the disparity in various tariff plans across states and country, proposed method will help to provide a platform for designing uniform tariff for entire country based on consumer’s electricity consumption data.
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Streamlining multitemporal vegetation indices for dependable crop growth monitoring in Himalayan foothill region
No full textMonitoring glacier surface velocity in Zanskar Valley, India: Insights from DInSAR-based 2D velocity estimation
No full textGlacier surface velocity plays a crucial role in understanding glacier dynamics, climate change impacts, and water resource management. In this study, Differential Synthetic Aperture Radar Interferometry (DInSAR) and geoinformatics techniques were employed to estimate the two-dimensional (2D) surface velocity of glaciers in Zanskar Valley, Ladakh, India. The analysis is based on C-band Sentinel-1 radar data acquired in both ascending and descending orbits to decompose the motion into horizontal and vertical components. The selected glaciers—Pensilungpa, Drang Drung, Khulka, and Kungi—exhibit varying velocity patterns, influenced by topography, ice thickness, and crevasse distribution. The results indicate that the Drang Drung Glacier, the largest in the study area, has the highest surface velocity, reaching approximately - 0.24 ± 0.02 in the upper accumulation zone. Pensilungpa Glacier exhibits distinct velocity variations, with rates of 0.07 ± 0.005 m/day near the equilibrium line altitude (ELA) and lower velocities near the terminus. The vertical and horizontal velocity components provide insights into the dominant glacier flow mechanisms, including ice deformation, sliding, and mass influx from tributaries. The study highlights the effectiveness of DInSAR for estimating glacier motion in complex mountainous terrain. The findings contribute to improved glacier monitoring and future ice thickness assessments, particularly for slow-moving glaciers. The methodology can be extended to other Himalayan glaciers and further refined using multi-frequency SAR data for enhanced accuracy. This research underscores the potential of satellite-based techniques for assessing glacier dynamics and their response to climate change
