Browsing by Author "Mathew, Aneesh"
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Item A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India(KeAi Communications Co. Ltd., 2024-02-11) Shekar, Padala Raja; Mathew, Aneesh; Yeswanth, P.V.; Deivalakshmi, SIn recent years, there has been a growing interest in using artificial intelligence (AI) for rainfall-runoff modelling, as it has shown promising adaptability in this context. The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed, India. These models included the artificial neural network (ANN), k-nearest neighbour regression model (KNN), extreme gradient boosting (XGBoost) regression model, random forest regression model (RF), convolutional neural network (CNN), and CNN-RNN (convolutional recurrent neural network). The years 2003–2007 are classified as the calibration or training period, while the years 2008–2009 are classified as the validation or testing period for the span of time 2003 to 2009. The available rainfall, maximum and minimum temperatures, and discharge data were collected and utilized in the models. To compare the performance of the models, five criteria were employed: R2, NSE, MAE, RMSE, and PBIAS. The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods (training: R2 is 0.99, NSE is 0.99, MAE is 1.76, RMSE is 3.11, and PBIAS is −1.45; testing: R2 is 0.97, NSE is 0.97, MAE is 2.05, RMSE is 3.60, and PBIAS is −3.94). These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study. The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management, flood control, and environmental planning.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 Artificial recharge sites unveiled: Geospatial-MCDM solutions for Akeru watershed, Telangana, India(Elsevier Inc., 2025-07-30) Shekar, Padala Raja; Mathew, Aneesh; Pramanik, Malay; Ben Hasher, Fahdah Falah; Zhran, MohamedGroundwater is vital for human health, agriculture, and ecological balance, making its sustainable management increasingly important amid rising demand. This study presents a geospatial and multi-criteria decision-making (MCDM) approach using the analytic hierarchy process (AHP) to identify suitable artificial recharge sites. Ten key thematic layers—drainage density, rainfall, topographic wetness index (TWI), curvature, elevation, geomorphology, topographic position index (TPI), distance from the river, land use and land cover, and slope—were selected based on their influence on groundwater recharge potential. Each layer was weighed using AHP, and the resulting normalized weights were integrated in a geographic information system (GIS) environment to delineate groundwater potential zones (GWPZs). The novelty of this research lies in overlaying the AHP-derived GWPZ map with identified artificial recharge locations, enabling precise site selection for recharge structures. The study area was classified into high, moderate, and poor recharge zones, with 74.6 % falling under moderate potential. Model validation using ground truth well locations and the area under the curve (AUC) method yielded a high prediction accuracy of 80.01 %, confirming the robustness of the methodology. A total of 176 suitable sites were identified, with recommendations for constructing percolation ponds and check dams. This approach enhances targeted groundwater recharge planning and supports sustainable water resource management. This research contributes directly to sustainable development goal 6 (clean water and sanitation) by promoting sustainable groundwater management and ensuring long-term water availability.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 Decadal Dynamics of Nighttime Urban Heat Island in Coimbatore: A Spatio-Temporal Investigation of Thermal Clustering and Intensification(Czech Society for Landscape Ecology, 2026-02-14) Gadekar, Kajesh; Mathew, Aneesh; Sarwesh, P; Naresh, C.This study presents a comprehensive spatio-temporal analysis of nighttime Land Surface Temperature (LST) and Urban Heat Island Intensity (UHII) in Coimbatore from 2001 to 2022, highlighting statistically significant warming trends and intensifying urban heat island effects. Urban areas experienced a notable nighttime LST increase from 21.4 °C in 2001 to 23.7 °C in 2019, compared to a rural rise from 20.5 °C to 22.5 °C. The average urban–rural LST differential (~1 °C) widened post-2016, aligning with the recorded peak LST of 26.8 °C. The minimum LST dropped to 8.5 °C in 2001, indicating a reduction in cold extremes. Kendall’s tau analysis confirmed a stronger warming trend in urban areas (τ = 0.593) than rural zones (τ = 0.429). Seasonal UHII analysis showed progressive winter intensification post-2012, while summer UHII peaked in 2013 and 2015, then dipped post-2016 before rising again in 2022. Mann-Kendall tests confirmed statistically significant increasing trends in winter UHII, urban LST, and rural LST, with urban LST exhibiting the steepest rise. Spatial autocorrelation analysis using Moran’s Index revealed intensifying clustering of high LST zones: the annual Moran’s Index increased from 0.797 (2001) to 0.857 (2022), with z-scores rising from 42.253 to 45.445. Winter showed the most pronounced clustering, with Moran’s Index jumping from 0.812 to 0.903 and z-scores reaching 47.848 by 2022. Hotspots with 99 % confidence levels were primarily urban, expanding over time with temperatures between 24.8 °C and 26.7 °C, while cold spots (99 % CL) remained stable in rural areas. These findings confirm the persistent and intensifying nature of UHI in Coimbatore, driven by urban expansion, declining vegetation, and increased impervious surfaces. This study fills a critical research gap by providing one of the first long-term assessments of nighttime UHI intensity in a mid-sized Indian city, thereby contributing to the broader understanding of urban thermal dynamics beyond metropolitan regions. The study underscores the urgent need for spatially informed interventions, such as urban greening, reflective materials, and climate-sensitive planning, to mitigate urban thermal stress and enhance resilience in rapidly growing cities.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 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 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 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 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 Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India(KeAi Communications Co., 2024-10-12) Lokesh, P; Madhesh, C; Mathew, Aneesh; Shekar, Padala RajaLandslide susceptibility mapping is vital for disaster management and sustainable land-use planning. This research was conducted in Wayanad, Kerala, India, to identify landslide susceptible zones. The study used large geospatial datasets, such as elevation, slope, aspect, curvature, stream power index, topographic wetness index, land use and land cover, rainfall, flow accumulation, geology, and geomorphology. It is followed by the application of various machine learning and deep learning models such as the support vector machine, artificial neural networks, logistic regression, random forest, gradient boosting machine, recurrent neural networks long short-term memory, and deep neural network models to map the landslide susceptible zones. The model was trained and validated using the landslide inventory map, which contains 298 sites of landslides. The random forest model, with 97 % accuracy, performed the best. It is possible to effectively mitigate landslides and plan long-term land use by identifying hazardous zones within the study region.Item Machine learning–based prioritization of sub-watersheds for soil erosion management: A case study of the Bardha watershed(Elsevier Inc, 2026-03-26) Shekar, Padala Raja; Mathew, Aneesh; Arun, P.S.; Prasanth, M. Surya; Alshehri, FahadSoil erosion is a major environmental concern that affects land productivity and water quality. Although soil erosion is a serious global environmental challenge, understanding its potential influence in the Bardha Watershed is important due to its topographical characteristics and the dependence of local communities on land resources for agriculture. Morphometric analysis helps assess a watershed's physical characteristics to understand its erosion potential. In this study, sub-watersheds were delineated using the shuttle radar topography mission (SRTM) digital elevation model (DEM) to accurately derive drainage and terrain characteristics. To enhance the precision of sub-watershed prioritization, morphometric analysis is combined with multi-criteria decisionmaking (MCDM) techniques. This research ranks sub-watersheds in the Bardha watershed in Chhattisgarh using morphometric parameters in combination with four MCDM approaches: additive ratio assessment (ARAS), multiobjective optimization by ratio analysis (MOORA), visekriterijumsko kompromisno rangiranje (VIKOR) and simple additive weighting (SAW). The criteria weights for these MCDM methods are determined using the criteria importance through intercriteria correlation (CRITIC) method. Furthermore, the novelty of this study lies in the integration of machine learning (ML) techniques, specifically support vector machine (SVM) and random forest (RF). By combining the outputs of all six methods, the study developed a unified priority map, which was subsequently classified into high, medium, and low priority zones. The study found that sub-watershed 3 (SW3) and SW4 fall into the common high-priority category; SW2, SW6, and SW7 into the medium category; and SW1 and SW5 into the low-priority group. This integrated method makes decision-making stronger by letting planners focus on high-priority sub-watersheds for strategic development, conservation, and optimal land management. This study aligns with SDG 15 by addressing land degradation through the identification and management of soil erosion-prone areas.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 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 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 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 Spatiotemporal dynamics of urban heat island effect and air pollution in Bengaluru and Hyderabad: implications for sustainable urban development(Springer Nature, 2025-02-25) Mathew, Aneesh; Aljohani, Taghreed Hamdi; Shekar, Padala Raja; Arunab, K. S.; Sharma, Atul Kumar; Ahmed, Mohamed Fatahalla Mohamed; Idris, Ummhani Idris Ahmed; Almohamad, Hussein; Abdo, Hazem GhassanUncontrolled growth in population is the cause of the unplanned, rapid, and unsustainable expansion of urban areas. This has led to a deterioration of environmental conditions for both global and local ecosystems. This research investigates the Urban Heat Island (UHI) phenomenon in Bengaluru and Hyderabad, India, including its spatial and temporal distribution and relation to air pollution. The investigation was conducted in both study locations during the summer and winter seasons, with data spanning from 2001 to 2021. The findings reveal that the maximum UHI intensity in both cities varies seasonally, with the highest values observed during the summer and the lowest during the winter. Annual maximum UHI intensities range from 4.65 °C to 6.69 °C in Bengaluru and from 5.74 °C to 6.82 °C in Hyderabad. The average UHI intensity also exhibits seasonal and annual variations, with the UHI effect being particularly pronounced in Bengaluru. In addition, the study provides the Urban Thermal Field Variance Index (UTFVI), which reveals that both cities consistently face intense UHI impacts throughout the year, greatly affecting the quality of life. Additionally, hotspot analysis reveals an increasing trend in UHI-affected areas over the years in both cities. The study also highlights air pollution concentrations and shows relationships between land surface temperature (LST) and air pollutants, emphasizing the need to alleviate urban heat, enhance air quality, and promote sustainability. This underscores the importance of UHI dynamics in urban environmental management and public health. This study enhances comprehension of UHI dynamics in swiftly urbanizing areas, providing a novel viewpoint on the complex interconnection between urbanization, climate, and air quality. These insights help develop sustainable urban strategies, reducing the negative effects of uncontrolled urbanization and benefiting local communities and the global ecosystem.Item Sub-watershed prioritization for soil erosion: a combined morphometric analysis, PCA, and MCDM approach(Springer Nature, 2025-09-25) Shekar, Padala Raja; Mathew, AneeshSoil erosion is a major global environmental problem, reducing soil fertility, crop yields, and causing economic losses. To tackle this effectively, it is essential to prioritize sub-watersheds using advanced techniques that support better planning and sustainable management. In this study, the delineation of the seven sub-watersheds (SWs) was carried out using a minimum third-order stream as the threshold. This study employs an integrated approach combining morphometric analysis, multiple criteria decision-making (MCDM) including additive ratio assessment (ARAS), technique for order of preference by similarity to ideal solution (TOPSIS), multi-objective optimization by ratio analysis (MOORA), simple additive weighting (SAW), and principal component analysis (PCA) to prioritize sub-watersheds in the Potteruvagu basin. The MCDM method used weights derived from the criteria importance through intercriteria correlation (CRITIC) method. The novelty of this study lies in its innovative application of MCDM techniques, synergistically combined with morphometric analysis and PCA for soil erosion priority. These novel methodologies enable precise and accurate analyses, facilitating the creation of a unified ranking system for each sub-watershed. The results classify SW5 and SW6 as high-priority soil erosion sub-watersheds, SW1 is a medium-priority soil erosion sub-watershed, and SW2, SW3, SW4, and SW7 are ranked low-priority soil erosion sub-watersheds. The results enable targeted soil erosion management, which directly helps with sustainable development goals (SDGs) such as SDG 6 (clean water and sanitation) and SDG 15 (life on land). This new framework makes it easier to make decisions based on facts for long-term planning and protection of watersheds.