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Item 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, AneeshThe 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.Item 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, AneeshLand-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.Item 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, SumitMany 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.Item 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, AneeshDue 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.Item 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, AneeshWater 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.Item 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 AbdullahThe 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.Item 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 AbdullahThe 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.Item 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 RajaClimate 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.Item 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, PAlterations 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.Item Watershed Prioritisation of Drainage Basins Based on Geomorphometric Parameters, Neyyar Watershed, India(Sciendo, 2023-06-27) Shekar, Padala Raja; Mathew, AneeshPrioritisation 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.Item Prioritizing sub‑watersheds for soil erosion using geospatial techniques based on morphometric and hypsometric analysis: a case study of the Indian Wyra River basin(Springer Nature, 2023-06-26) Shekar, Padala Raja; Mathew, Aneesh; Abdo, Hazem Ghassan; Almohamad, Hussein; Abdullah Al Dughairi, Ahmed; Al-Mutiry, MotrihThe hydrological availability and scarcity of water can be affected by geomorphological processes occurring within a watershed. Hence, it is crucial to perform a quantitative evaluation of the watershed’s geometry to determine the impact of such processes on its hydrology. Geographic information systems (GIS) and remote sensing (RS) techniques have become increasingly significant because they enable decision-makers and strategists to make accurate and efficient decisions. To prioritize sub-watersheds within the Wyra watershed, this research employs two methods: morphometric analysis and hypsometric analysis. The watershed was divided into eleven sub-watersheds (SWs). The prioritization of sub-watersheds in the Wyra watershed involved assessing several morphometric parameters, such as relief, linear, and areal features, for each sub-watershed. Furthermore, the importance of the sub-watersheds was determined by computing hypsometric integral (HI) values using the elevation–relief ratio method. The final prioritization of sub-watersheds based on morphometric analysis was determined through the integration of principal component analysis (PCA) and weighted sum approach (WSA). SW2 and SW9 have had higher priorities using morphometric analysis, whereas SW6, SW7, and SW10 have obtained higher priorities using hypsometric analysis. SW4 is the most common SW that shares the same priority. The most vulnerable sub-watersheds are those with the highest priority, and therefore, programmes for soil and water conservation should pay more attention to them. The conclusions of the study may prove useful to various stakeholders involved in initiatives related to watershed development and management.Item Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine learning approaches in the Peddavagu River Basin, India(IWA Publishing, 2023-06-05) Shekar, Padala Raja; Mathew, Aneesh; Arun, P. S.; Gopi, Varun P.Water resource management is critical in the face of climate change to reduce water scarcity and meet the demands of an expanding population. Prioritization of watersheds has gained significance in natural resource management, particularly in the context of watershed management. This study prioritizes sub-watersheds for the Peddavagu basin using five methods. The four methods mentioned above can be estimated utilizing remote sensing (RS) and geographic information system (GIS) approaches, while linear discriminant analysis (LDA) is estimated using machine learning techniques. The catchment resulted in the formation of 13 sub-watersheds. The quantitative measurements of morphometric analysis, including linear, relief, and areal, were considered, and 18 morphometric characteristics were chosen to rank and prioritize sub-watersheds. Principal component analysis (PCA) was used to rank and prioritize sub-watersheds based on four highly correlated morphometric parameters. The land use/land cover (LULC) analysis used four features to prioritize sub-watersheds. The LDA analysis used two features to prioritize sub-watersheds. Using hypsometric integral (HI) values, prioritization has been done. Sub-watersheds were prioritized. Based on five methods, the sub-watersheds were classified as low, medium, and high. Among the sub-watersheds identified as high priority, immediate priority is assigned to SW10. Decision-makers in the research region can use the findings to plan and implement watershed management techniques.Item Geo-physical seasonal deviations of land use, terrain analysis, and water cooling effect on the surface temperature of Pune city(IWA Publishing, 2023-12-12) Sharma, Kul Vaibhav; Kumar, Vijendra; Gautam, Lilesh; Choudhary, Sumit; Mathew, AneeshUrban heat islands are hotter than rural places. Sustainable urban growth and improving urban environments need understanding Urban Heat Island (UHI) causes and finding effective mitigation techniques. This research examines the seasonal deviations in surface temperatures for the UHI effect in Pune, India, focusing on land use patterns and water body cooling. Land use categorization included residential, commercial, industrial, vegetation, and open spaces. The research studied the cooling potential and temperature variance by distance from water bodies in the form of lakes, rivers, and ponds. These aquatic bodies have surface and ambient temperature sensors. Roads, soil, commercial areas, residential areas, industrial areas, and vegetation have all shown increases in NDBI, ranging from 15.84 to 36.45%. Urban regions with heat accumulation and dissipation have been revealed by DEM and contour maps. The research found that the water bodies have a cooling effect on LST till the distance of 350 m. The research finds hotter places and shows how natural features mitigate UHI by analyzing land use patterns and water body cooling. The findings emphasize the significance of green areas and water bodies in urban design and development to improve Pune's climate resilience and inhabitability.Item GIS-based assessment of soil erosion and sediment yield using the revised universal soil loss equation (RUSLE) model in the Murredu Watershed, Telangana, India(KeAi Communications Co. Ltd., 2024-05-17) Shekar, Padala Raja; Mathew, AneeshThe current investigation was conducted in the Murredu watershed, situated in India. The essential datasets, such as the digital elevation model (DEM), soil, land use land cover (LULC), and rainfall parameters, were processed and analysed using a Geographic Information System (GIS) environment. The current research utilised the revised universal soil loss equation (RUSLE) model to assess the mean soil loss in the Murredu watershed. The mean annual soil loss was calculated to be 14.06 t/ha/year, indicating a high soil erosion risk. The RUSLE model results indicated a good outcome with an accuracy of 72.8%. Furthermore, the research area revealed that sub-watersheds (SW) 2 and SW 14 had the maximum and minimum mean annual soil loss, respectively. The sediment delivery ratio (SDR) for the Murredu watershed was determined to be 0.227. The Murredu watershed outlet received a mean annual sediment yield of 3.19 t/ha/year. Through investigation, it was determined that SW 2 had the maximum mean annual sediment yield, while SW 11 had the minimum. This current investigation provides valuable insights for stakeholders, decision-makers, and policymakers regarding sustainable ways of managing watersheds.Item Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India(AGH University of Science and Technology Press, 2024-01-31) Nandan, A K; Mathew, Aneesh; Shekar, Padala RajaAir pollution is one of the grave concerns of the modern era, claiming millions of lives and adversely impacting the economy. Aerosols have been observed to play a significant role in negatively influencing climatological variables and human health in given areas. The current study aimed to study the trend of aerosols and particulates on daily, monthly, seasonal, and annual levels using a 20-year (2002–2021) daily mean aerosol optical depth (AOD) product released by moderate resolution imaging spectrometer (MODIS) sensors for the Hyderabad district in India. The results of the daily mean analysis revealed a rising trend in the number of days with severe AOD (>1), whereas examinations of the seasonal and monthly mean data from 2017 through 2022 showed that peak AOD values alternated between the summer, autumn, and winter seasons over the years. Trend analysis using Mann–Kendall, modified Mann–Kendall, and innovative trend analysis (ITA) tests revealed that AOD increased significantly from 2002 through 2021 (p < 0.05; Z > 0). Furthermore, correlation analysis was performed to check for correlations between AOD levels and certain meteorological factors for the Charminar and Secunderabad regions; it was noticed that temperature had a weak positive correlation with AOD (p < 0.05; r = 0.283 [Secunderabad] – p < 0.05; r = 0.301 [Charminar]), whereas relative humidity developed a very weak negative correlation with AOD (p < 0.05; r = −0.079 [Secunderabad] – p < 0.05; r = −0.109 [Charminar]).Item Water distribution system modelling of GIS-remote sensing and EPANET for the integrated efficient design(IWA Publishing, 2024-03-05) Dongare, Pranit; Sharma, Kul Vaibhav; Kumar, Vijendra; Mathew, AneeshUrban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyse and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for the management of the Bota town’s water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analysing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, RS, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town’s water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, RS, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues. © 2024 The Authors.Item Sustainable data-driven insights: Statistical analysis and artificial intelligence-driven modelling of aerosol concentrations in Hyderabad district, India(Elsevier Ltd., 2024-04-29) Nandan, A K; Mathew, AneeshAir pollution stands as a pressing issue in contemporary times, leading to the loss of millions of lives and exerting detrimental effects on the economy. The aerosols especially particulate matter, which are dispersions of matter in air medium play an important role in manipulating the climatological variables in an area. The current study was developed in response to the need to study aerosols and particulates on annual levels using 20-year (2002–2021) daily mean Aerosol Optical Depth (AOD) product released by Moderate Resolution Imaging Spectrometer (MODIS) sensors, and to generate prediction models for AOD using artificial intelligence (AI) techniques for Hyderabad district in India. The results of daily mean analysis revealed a rising trend in the number of days with severe AOD (> 1). Yearly mean AOD distribution showed a percentage increase of 45.31 % from 2002 to 2021. Furthermore, factor analysis was carried out to check for correlations of AOD and PM2.5 with various meteorological and pollutant variables. It was observed that both PM2.5 and AOD had significant weak to moderate (p < 0.05; r < 0.5) correlations with both pollutants and meteorological variables. The hybrid deep learning-based CNN-LSTM was identified as the best-fit model to predict AOD, outperforming MLP – ARIMA and MLP models. CNN – LSTM showed an R2 of 0.70, MAE of 0.08, MSE of 0.02 and RMSE of 0.14.Item Exploring spatial machine learning techniques for improving land surface temperature prediction(Elsevier B.V., 2024-05-05) Arunab, K.S.; Mathew, AneeshLand Surface Temperature (LST) is a crucial parameter in Earth observation and environmental studies due to its significance in various fields. The purpose of this study is to investigate the effects of including spatial information into the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models for forecasting LST. The significance and impact of each input parameter on the models' predictive capabilities are assessed using the SHAP (Shapley Additive exPlanations) approach and the model intercomparisons were done using the error evaluation metrices. The predictions were further validated using the Pearson correlation, independent samples t-test and potential geographic anomalies in the predictions are examined by spatial comparison of predicted errors using classification maps and error envelopes. The projected errors are within the acceptable range and range from −2.267 °C to 1.292 °C for the spatially enhanced RF model and from −1.675 °C to 1.439 °C for the spatially enhanced XGBoost model. These error ranges closely align with the training data's quality flag of ±2 °C, demonstrating the models' capability to predict LST accurately and within a reasonable error range. The findings show the significance of adding spatial information for precise LST prediction and draw attention to possible uses for such models in environmental monitoring and management. The work advances our understanding of spatial modelling strategies and offers practical guidelines for enhancing LST forecasts.Item Assessment of soil erosion and sediment yield in the Peddavagu watershed, India, using a revised universal soil loss equation model (RUSLE) and GIS techniques(IWA Publishing, 2024-05-12) Shekar, Padala Raja; Mathew, AneeshThe present investigation was carried out within the Peddavagu watershed, which is located in India. The necessary datasets, including soil, land use land cover, rainfall, and digital elevation model, were processed and analysed within a Geographic Information System framework. To evaluate soil loss within the watershed, the present investigation employed the revised universal soil loss equation (RUSLE) model. Subsequently, the sediment yield is estimated based on the sediment delivery ratio (SDR). The average annual soil loss was estimated at 17.91 tonnes/hectare/year, which is high soil erosion risk. The RUSLE model's accuracy is 82.1%. Moreover, the findings revealed that sub-watersheds (SW) 9 and SW 3 exhibited the maximum and minimum average annual soil loss. The Peddavagu watershed's SDR was 0.210. Annually, 3.76 tonnes/hectare/year of sediment were transported to the Peddavagu watershed outlet. The findings revealed that SW 9 and SW 5 exhibited the maximum and minimum average annual sediment yield. The model's performance was evaluated by comparing its predictions with gauge data for validation. The observed actual data indicated a yield of 3.66 tonnes/hectare/year, while the model predicted a yield of 3.76 tonnes/hectare/year. This resource offers significant insights for policymakers and decision-makers on sustainable watershed management techniques.Item Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis(Nature portfolio, 2024-09-16) Mathew, Aneesh; Shekar, Padala Raja; Nair, Abhilash T; Mallick, Javed; Rathod, Chetan; Bindajam, Ahmed Ali; Alharbi, Maged Muteb; Abdo, Hazem GhassanIn India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.