National Institute of Technology Tiruchirappalli
Central Library
Institutional Digital Repository
Powered by National Digital Library of India (NDLI)
Communities in DSpace
Select a community to browse its collections.
Recent Submissions
Morphometric analysis for prioritizing sub‑watersheds of Murredu River basin, Telangana State, India, using a geographical information system
(Springer, 2022-05-16) Shekar, Padala Raja; Mathew, Aneesh
The Murredu watershed in Telangana State was chosen for the morphometric and land use/land cover (LULC) analysis in this current study. Geographical information system (GIS) and remote sensing (RS) techniques can estimate the morphometric features and LULC analysis of a catchment. A total of fourteen sub-watersheds (SWs) were created from the watershed (SW 1 to SW 14), and sub-watersheds were prioritized based on morphometric and LULC features. Evaluation of various morphometric characteristics such as linear aspects, relief aspects, and aerial aspects has been carried out for every sub-watershed to prefer ranking. Four parameters were utilized for the LULC analysis to rank and prioritize sub-watersheds. The sub-watersheds were categorized into three groups as low, medium, and high, for soil and water conservation priority based on morphometric and LULC analysis. Using morphometric analysis, higher priorities have been assigned to SW 12 and SW 1, while using LULC analysis, higher priorities have been assigned to SW 9 and SW 11. SW 10 and SW 13 are the most common sub-watersheds that fall within the same priority while using morphometric and LULC analysis. The coefficient of regression results reveals that stream length and stream order, and also stream number and stream order, have a strong association. The deployment of soil and water conservation measures may be conducted in the high-priority sub-watersheds.
Detection of land use/land cover changes in a watershed: A case study of the Murredu watershed in Telangana state, India
(KeAi Communications, 2022-12-20) Shekar, Padala Raja; Mathew, Aneesh
Land-use change refers to a change in how a particular area of land is utilised or managed by humans. Land-cover change refers to a change in some continuous features of the land, such as vegetation type, soil conditions, and so on. For the purpose of identifying change-vulnerable areas and creating sustainable ecosystem services, mapping and quantifying the state of land use/land cover (LULC) changes and change-causing factors are crucial. The present research utilizes a geographic information system (GIS) and remote sensing (RS) techniques to categorise and identify changes in a Murredu watershed in Telangana state, India, between 1996 and 2019. Five major LULC categories (agricultural land, forest, barren land, built-up area, and waterbodies) from satellite images of 1996 to 2019 were mapped. The maximum likelihood approach was used to supervise the classification process, and high-resolution Google Earth Pro was used to evaluate the accuracy of the classified map. The accuracy of the mapping was evaluated using the error matrix and Kappa statistics. Overall classification accuracy for the classified image of 2019 was found to be 90 % with overall kappa statistics of 85.98%. From these findings, change detection analysis shows that the area used for agricultural land, barren land, forest, built-up areas, and waterbodies has increased by 5.17%, 3.39%, 0.84%, and 0.26%, respectively, between 1996 and 2019. The forest area has decreased by 9.67% at the same time. Therefore, this research anticipates that the findings might provide information to planners, land managers, and decision-makers for the sustainable management and development of the natural resource.
Investigating the contrast diurnal relationship of land surface temperatures with various surface parameters represent vegetation, soil, water, and urbanization over Ahmedabad city in India
(Elsevier Ltd, 2022-01-30) Mathew, Aneesh; Sarwesh, P; Khandelwal, Sumit
Many climatic problems have arisen due to congested and inefficient planning, reduced vegetation cover, and increased pollution from factories and vehicles. One such primary concern is increased land surface temperature (LST) contributes to the urban heat island (UHI) occurrence. This research aims to understand better the UHI effect in the region neighbouring the Indian city of Ahmedabad. MODIS sensor data (onboard Aqua and Terra platforms) and Landsat data were used for the study. The research was done for the summer, monsoon, and winter seasons in the research region, using data from thirteen years between 2003 and 2015. The current study looked at LSTs' spatial and temporal differences to assess the SUHI effect over Ahmedabad city. The association between diurnal LST and various surface variables such as vegetation, built-up, soil, water, and so on has also been examined. A variety of land surfaces influences the diurnal variations of LSTs. The diurnal associations of LST with vegetation, urbanization, soil, and water factors have been studied. The overall study of LST' relationship with all of the various parameters reveals a very significant dynamic relationship.
Prioritising sub-watersheds using morphometric analysis, principal component analysis, and land use/land cover analysis in the Kinnerasani River basin, India
(IWA Publishing, 2022-08-30) Shekar, Padala Raja; Mathew, Aneesh
Due to the depletion of natural resources including land and water as a result of rapid population increase, industrialisation, and urbanisation, effective resource management is essential for long-term development. The Kinnerasani Watershed in Telangana State was chosen for the research based on morphological analysis, principal component analysis (PCA), and land use/land cover (LULC) analysis in this study. A catchment’s morphometric characteristics, PCA, and LULC analysis can be estimated using geographic information system (GIS) and remote sensing (RS) approaches. The watershed generated 24 sub-watersheds (SWs) in all (SW1–SW24). SWs were ranked using morphometric features, PCA, and LULC features. To determine the final priority of SWs, several morphometric characteristics, including linear, shape, and relief aspects, have been estimated for each SW and given ranks based on compound parameter values. To prioritise SWs, the PCA was used to extract five parameters from morphometric characteristics. The LULC analysis used four characteristics to prioritise the SWs. SW3, SW9, and SW12 have been prioritised for morphometric analysis; SW2 and SW3 have been prioritised for PCA; and SW17, SW19, SW23, and SW24 have been prioritised for LULC analysis. The common SWs within each priority according to three different methodologies are SW4, SW6, SW10, SW13, SW15, and SW21. The results show that the high-priority locations have greater runoff and soil erosion issues, so it is essential to design and implement watershed management techniques such as check dams, construction of farm ponds, and construction of earthen embankments in these areas. The decision-making authorities might use the findings to plan and implement watershed management initiatives to minimise soil erosion in high-priority locations. © 2023 International Journal of Mining and Geo-Engineering. All rights reserved.
Evaluation of Morphometric and Hypsometric Analysis of the Bagh River Basin using Remote Sensing and Geographic Information System Techniques
(Elsevier, 2022-06-24) Shekar, Padala Raja; Mathew, Aneesh
Water availability and scarcity are impacted by geomorphological changes that occur within a catchment. As a result, determining the influence of geomorphological processes on the catchment's hydrology requires a quantitative study of the catchment geometry. Approaches based on remote sensing (RS) and geographic information systems (GIS) have grown in popularity in recent years because they assist strategists and decision-makers in making accurate and effective choices and plans. For this research, the Bagh River basin was chosen. The study shows that GIS and RS data can be used to analyse and approximate the period and erosional operations' speed in a Bagh river basin for better design and maintenance. The method utilises a 30-metre shuttle radar topography mission digital elevation model (SRTM-DEM) for morphometric parameters and hypsometric analysis extraction that is both operative and time-saving. The thirteen morphometric parameters were applied to the Bagh catchment's linear, shape, and relief aspects. An elongated basin shape is suggested by the Re, Rc, and Ff. Statistical analysis shows that there is a good relationship between stream order and stream length, as well as stream order and stream number. The hypsometric curves' structure as well as estimated hypsometric integral results reflects the Bagh river basin's erosional stages. As a result, the study concludes that morphometric and hypsometric analysis findings may be useful to stakeholders participating in catchment development and management projects.
Thermal dynamics of Jaipur: Analyzing urban heat island effects using in-situ and remotely sensed data
(Cogent OA, 2023-11-02) Mathew, Aneesh; Sarwesh, P; Khandelwal, Sumit; Shekar, Padala Raja; Alao, Joseph Omeiza; Abdo, Hazem Ghassan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah
The Urban Heat Island (UHI) effect is a phenomenon where urban areas experience higher temperatures than surrounding rural areas. In these issues, enhanced air or surface temperature is one of the major issues that led to the UHI phenomenon. In this article, we come up with a study on the diurnal UHI effect caused in Jaipur city, India, and surrounding areas of Jaipur. In-situ temperature monitoring has been carried out at seven dispersed locations to properly understand and evaluate the effects of surface UHI (SUHI) and atmospheric UHI (AUHI), as well as to assess the thermal profile of diverse land surfaces in Jaipur. With the use of satellite data, the intensity of AUHI and SUHI has been determined between 10.30 a.m. and 10.30 p.m. The observations point out that positive AUHI intensity (AUHII) exists at many locations, irrespective of time periods. During the day period, negative SUHI intensity (SUHII) was noticed at many locations, whereas during the night period, positive SUHII was noticed. According to our observations, AUHI and SUHI have a direct correlation at night but a negative or inverse correlation during the day. That is, AUHI was active both during the day and at night. Various land surfaces play a significant role in contrasting the diurnal UHI effect. This study evaluates the potential of remotely sensed data in monitoring the UHI effect and provides recommendations for urban planners and policymakers to mitigate the UHI effect in the city of Jaipur.
Air quality analysis and PM2.5 modelling usingmachine learning techniques: A study of Hyderabad city in India
(Cogent OA, 2023-08-13) Mathew, Aneesh; Gokul, P R; Shekar, Padala Raja; Arunab, K S; Abdo, Hazem Ghassan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah
The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. A study conducted by the Swiss organization IQAir indicated that 22 of the top 30 most polluted cities in the world are in India. This creates the problem of air pollution, which is very relevant to India as well. Exposure to air pollutants has both acute (short-term) and chronic (long-term) impacts on health. Among the major air pollutants, particulate matter 2.5 (PM2.5) is the most harmful, and its long-term exposure can impair lung functions. Pollutant concentrations vary temporally and are dependent on the local meteorology and emissions at a given geographic location. PM2.5 forecasting models have the potential to develop strategies for evaluating and alerting the public regarding expected hazardous levels of air pollution. Accurate measurement and forecasting of pollutant concentrations are critical for assessing air quality and making informed strategic decisions. Recently, data-driven machine learning algorithms for PM2.5 forecasting have received a lot of attention. In this work, a spatio-temporal analysis of air quality was first performed for Hyderabad, indicating that average PM2.5 concentrations during the winter were 68% higher than those during the summer. Following that, PM2.5 modelling was done using three different techniques: multilinear regression, K-nearest neighbours (KNN), and histogram-based gradient boost (HGBoost). Among these, the HGBoost regression model, which used both pollution and meteorological data as inputs, outperformed the other two techniques. During testing, the model acquired an amazing R2 value of 0.859, suggesting a significant connection with the actual data. Additionally, the model exhibited a minimum Mean Absolute Error (MAE) of 5.717 μg/m3 and a Root Mean Square Error (RMSE) of 7.647 μg/m3, further confirming its accuracy in predicting PM2.5 concentrations. In our investigation, we discovered that the HGBoost3 model beat other PM2.5 modelling models by having the lowest error and the highest R2 value. This study made a substantial addition by incorporating the spatiotemporal relationship between air pollutants and meteorological variables in predicting air quality. This method has the potential to improve the creation of more precise air pollution forecast models.
Rainfall and temperature dynamics in four Indian states: A comprehensive spatial and temporal trend analysis
(KeAi Communications Co., 2023-09-09) Nath, Subrat; Mathew, Aneesh; Khandelwal, Sumit; Shekar, Padala Raja
Climate change poses a significant global challenge, impacting rainfall and temperature patterns worldwide. To assess regional and temporal changes, we conducted a trend analysis on mean monsoon rainfall and mean summer temperature in four Indian states with diverse climates: Karnataka, Gujarat, Rajasthan, and Maharashtra. The selection of these states as study areas was based on the monsoon's arrival time from the Arabian Sea. Using nonparametric statistical trend analysis techniques such as the Mann-Kendall test, Sen's slope estimator, Kendall tau and Mann-Whitney-Pettitt (MWP). we examined trends from 1951 to 2000 at a significance level of 5%. Additionally, we employed linear regression to identify climatic patterns. Our findings revealed both positive and negative trends in mean monsoon rainfall and mean summer temperature across all four states. Rainfall trends exhibited a decreasing pattern in all states, except for Maharashtra, which displayed a slightly negative trend despite an overall positive annual temperature trend. Conversely, temperature trends showed an increasing pattern in all states except Maharashtra. To further explore the relationship between summer temperature and monsoon precipitation, we investigated several urban centers within these four states. The results indicated varying trends, including increasing, decreasing, and no discernible trend across different stations. Our analysis demonstrated a general decline in yearly monsoon precipitation across most regions in the four states, coupled with recorded temperature changes. Notably, Karnataka exhibited a stronger positive correlation between rainfall and temperature trends. Maharashtra and Gujarat also exhibited a positive correlation, albeit at a moderate level. Conversely, Rajasthan displayed a very weak correlation (tau = 0.079), indicating no significant relationship between these two climatic parameters.
Spatial and temporal analysis of urban heat island effect over Tiruchirappalli city using geospatial techniques
(KeAi Communications Co., 2022-12-09) Badugu, Ajay; Arunab, K S; Mathew, Aneesh; Sarwesh, P
Alterations made to the natural ground surface and the anthropogenic activity elevate the surface and air temperature in the urban areas compared with the surrounding rural areas, known as urban heat island effect. Thermal remote sensors measure the radiation emitted by ground objects, which can be used to estimate the land surface temperature and are beneficial for studying urban heat island effects. The present study investigates the spatial and temporal variations in the effects of urban heat island over Tiruchirappalli city in India during the summer and winter seasons. The study also identifies hot spots and cold spots within the study area. In this study, a significant land surface temperature difference was observed between the urban and rural areas, predominantly at night, indicating the presence of urban heat island at night. These diurnal land surface temperature fluctuations are also detected seasonally, with a relatively higher temperature intensity during the summer. The trend line analysis shows that the mean land surface temperature of the study area is increasing at a rate of 0.166 K/decade with p less than 0.01. By using the spatial autocorrelation method with the urban heat island index as the key parameter, hot spots with a 99 percent confidence level and a 95 percent confidence level were found within the urban area. A hot spot with 95 and 90 percent confidence level was identified outside the urban area. This spike in temperature for a particular region in the rural area is due to industry and the associated built-up area. The study also identified cold spots with a 90 percent confidence level within the rural area. However, cold spots with a 95 and 99 percent confidence level were not identified within the study area.
Watershed Prioritisation of Drainage Basins Based on Geomorphometric Parameters, Neyyar Watershed, India
(Sciendo, 2023-06-27) Shekar, Padala Raja; Mathew, Aneesh
Prioritisation of sub-watersheds (SWs) is becoming increasingly important in the conservation of natural resources, particularly in watershed planning. In this study, sub-watershed for the Neyyar basin was prioritised using three methods: morphometric analysis, principal component analysis (PCA) and hypsometric analysis. Morphometric analysis and hypsometric analysis were carried out using remote sensing (RS) and geographic information system (GIS) techniques, while PCA was performed for dimensionality reduction of morphometric parameters. The watershed was divided into 11 sub-watersheds (SW1–SW11), and each sub-watershed was given priority. To rank and prioritise SWs, 15 morphometric parameters were selected from the quantitative measures of morphometric analysis, including linear, relief, and areal. PCA was used to rank and prioritise SWs based on three highly correlated morphometric parameters. The hypsometric integral (HI) values were determined using the elevation relief ratio approach, and HI values were utilised to prioritise SWs. For both methods, such as morphometric analysis and PCA, a higher priority has been given to SW1. Using hypsometric analysis, higher priorities have been assigned to SW1, SW7, SW8, SW9, SW10 and SW11. The most common SWs that belong to the same priority of SWs and have a high correlation between them among the three methods are SW1, SW2, and SW5.The results of this analysis indicate that SW1 is a common high priority area with a significant risk of soil erosion, runoff and peak discharge. Therefore, decision-makers may utilise the high-priority sub-watershed to guide planning and development, measure conservation efforts and manage the land to prevent.