Search

Decline in the use of public transit by commuters have increased the use of private vehicles, causing higher levels of traffic congestion, accidents, etc. The present study aims to identify major latent attributes influencing the behaviour of government employees working in the study area by using an integrated mode choice model. The unobservable attributes that influence mode selection decisions were analysed using the semantic differential technique and a five-point bipolar adjective scale. Conventional mode choice models and latent variable integrated mode choice models were developed for four different modes. Sensitivity analysis was carried out to assess the impact of significant variables which has revealed that 15% decrease in travel time on public transport could lead to a 17% increase in ridership. This study also identified significant variables that influence mode selection decisions and formulated policies to increase the use of public transport in medium-sized cities. [ABSTRACT FROM AUTHOR]

Public transport ridership has fallen drastically over the past few years, resulting in an increased number of private vehicles on the road, negatively impacting the road infrastructure with escalated levels of traffic congestion, pollution, road accidents, etc. Policy interventions provide a viable solution to the problem of ridership as they influence the decision-making of the public. In this article, an attempt was made to model the choice behaviour of employees working in the city of Thiruvananthapuram, India based on soft computing techniques. The choice data were collected through a revealed preference survey and mode choice models were developed using Artificial Neural Networks (ANN), Neuro-Fuzzy, and Fuzzy logic taking socio-economic characteristics, trip characteristics, and the identified latent factors as inputs. The results from the developed models are encouraging and they show good statistical validity. To formulate passenger attraction policies, the major latent factors that affect public bus ridership rates were examined. Consequently, suitable transport improvement policies are recommended based on the most influencing latent factors. Finally, based on the results of the sensitivity analysis, several policy interventions to enhance public transport ridership are recommended. [ABSTRACT FROM AUTHOR]

This paper gives an overview of a major application of modern geospatial tools such as remote sensing, GIS and GPS, i.e., 3S technology in estimating travel demand along Indian roads by considering the study area, Tiruchirappalli urban city in Tamil Nadu. In the study, an attempt was made to estimate travel demand based on the current land-use classification as the pattern of travel depends on the type of land use activity in a zone. IRS high resolution image Cartosat-1 of year 2009 was used to extract the land-use information required. The travel demand model developed was validated with the field obtained OD-matrix to understand the accuracy of the method considered. [ABSTRACT FROM AUTHOR]

Land-use/land-cover (LU/LC) change is an important component of global environmental change. The need to understand LU/LC change is essential for regional development and land use management towards sustainable development. To understand LU/LC change, the different LU/LC categories and their spatial as well as temporal variability in Tiruchirappalli city has been studied over a period of eight years (1998–2006), using the analysis of Indian Remote Sensing Satellite (IRS) images. In this, an attempt was made to adopt Markov model for obtaining and understanding LU/LC dynamics. Model performance was evaluated between the empirical LU/LC map extracted from CARTOSAT-1 PAN image and the simulated LU/LC map obtained from the Markov model. The future landscape distribution in the year 2014 and 2022 was derived using a Markov model. This result shows that Markov model and geospatial technology together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization in this region. [ABSTRACT FROM PUBLISHER]

Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The SINTACS model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. The SINTACS model uses seven environmental parameters (water table depth, effective infiltration, unsaturated conditions, soil media, aquifer hydrogeologic characteristics, hydraulic conductivity and topographic slope) to characterize the hydrogeological setting and evaluate aquifer vulnerability. In this paper, the SINTACS model is applied for a part of Kancheepuram district, Tamil Nadu, India to generate a small-scale map of groundwater vulnerability to contamination. The whole area is classified as very low, low, moderate and high susceptibility to pollution. The model is validated with ground water quality data and results have shown strong relationship between SINTACS specific vulnerability index and nitrate-as-nitrogen concentrations. The ground water vulnerability map is developed by using the SINTACS model in a computer-based geographic information system (GIS). [ABSTRACT FROM PUBLISHER]

Land use/land cover mapping serves as a basic inventory of land resources throughout the world. Detecting and analyzing the quantitative changes along the earth's surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, and economic and industrial growth. This paper examines the changes in land use/land cover types in Tiruchirappalli city, Tamil Nadu from 1998-2006, using remote sensing technology and Geographic Information Systems (GIS). The spatial and temporal changes of land use/land cover were analyzed through visual interpretation and image classification of IRS remote sensing data collected for years 1998, 2002 and 2006. The results of the study indicated an overall growth in built-up land by 80% over a span of eight years, with an incredible boost (58%) in development from 1998-2002. The area under agricultural wet land was found to have decreased from 3,776 ha to 2,689 ha, with an increase in agricultural dry land from 814 ha to 1,058 ha during this period, suggesting the wastage of land due to excessive use of fertilizers. [ABSTRACT FROM AUTHOR]

The paper gives a broad overview of the work carried out by the Indian Institute of Technology Bombay (IITB) in determining the post-seismic effect of the December 26, 2004 Sumatra-Andaman earthquake on the Indian plate, using space-based technology, the GPS. A GPS network established by the IITB along the western coast of the Indian peninsular was considered for this purpose, observed and analyzed thoroughly, in order to investigate the potential of GPS technology in studying Earth dynamics. To understand the pattern of movement of the Earth's surface to the effect of the large Sumatra-Andaman earthquake, the magnitude and, most importantly, the direction of deformation of the GPS network were estimated. The GPS-estimated deformations were thereby used as indicators to identify any change in the Earth surface movement due to the large external force caused by the 2004 earthquake. [ABSTRACT FROM AUTHOR]

On June 8, 2005, the Koyna region in Western Maharashtra, India, was rocked by a magnitude 4.2 earthquake. This earthquake was followed by an aftershock of M 3.6. The locations of the two earthquakes were about 20 km SE of the dam situated in Koyna. For monitoring the deformation of the dam, a GPS receiver was set up on the top of the dam by Indian Institute of Technology Bombay (IITB), which was continuously operating even during the time of occurrence of the earthquakes. The GPS group of IITB has been working over six years in the field of deformation measurements and analysis of Koyna Dam under a research project funded by the Department of Science and Technology, Government of India. A GPS network has been established for this purpose, and observed periodically in order to investigate the potential of GPS Technology in deformation measurement analysis. The data collected by the GPS receiver on June 8, 2005 was used to analyze the movement of the dam due to the earthquakes. The analysis of the data collected just before and after the earthquake occurrences showed a displacement in the NW direction with a certain magnitude of deflection. After the occurrence of the earthquakes, the dam was observed to attain its original position by shifting in the opposite direction (SE) with the same degree of deflection as observed during the time of the earthquakes. These results reflect the reflex movement of the dam body and its response to the occurrence of the main shock and its aftershock. These results indicate the high stability of the Koyna Dam. [ABSTRACT FROM AUTHOR]

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)