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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, S
In 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.
Adsorptive removal of Pb(II) from aqueous solution using nano-sized hydroxyapatite
(Springer Verlag, 2012-10-11) Ramesh, S.T.; Rameshbabu, N; Gandhimathi, R; Srikanth Kumar, M; Nidheesh P.V.
This study investigated the sorption of Pb(II) in aqueous solution onto hydroxyapatite (HA) surfaces. Batch experiments were carried out using synthetic HA. The effect of contact time, HA dosage, and initial pH on removal efficiency were also investigated. The adsorption equilibrium and kinetics of Pb(II) on this adsorbent were then examined at 25 °C. Kinetic data were analyzed by pseudo first, second, and intra-particle diffusion models. The sorption data were then correlated with the Langmuir, Freundlich, Halsey, and Harkins-Jura adsorption isotherm models. The optimum dose of HA for Pb(II) removal is found to be 0. 12 g/l with the removal efficiency of 97. 3 % at an equilibrium contact time of 1 h. It is found that the adsorption kinetics of the Pb(II) on HA follow the pseudo second-order reaction. All the isotherms fitted well for experimental data. Capacity of HA is found as 357. 14 mg Pb(II)/g of HA. The Pb(II) immobilization mechanism was studied. The results indicated that HA can be used as an effective adsorbent for removal of Pb(II) from aqueous solution. © 2012 The Author(s).
Advancing Agricultural Land Suitability in Urbanized Semi-Arid Environments: Insights from Geospatial and Machine Learning Approaches
(Multidisciplinary Digital Publishing Institute (MDPI), 2024-12-03) Sathiyamurthi, Subbarayan; Subbarayan, Saravanan; Ramya, Madhappan; Sivasakthi, Murugan; Gobi, Rengasamy; Qaysi, Saleh; Praveen Kumar, Sivakumar; Lee, Jinwook; Alarifi, Nassir; Wahba, Mohamed; M. Youssef, Youssef
Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries. © 2024 by the authors.
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.
An assessment of brick masonry strengthening practice by special methods
(Blue Eyes Intelligence Engineering and Sciences Publication, 2019-09-03) Kanchidurai, S; Krishnan, P.A.; Baskar, K
This study aims to identify the best suitable method to enhance strength and the structural performance of masonry. There are different techniques available to strengthen the existing and new masonry structures. This paper deals the metal/mesh embedment in the masonry wall, strengthening by added different polymers and textile strips, masonry grout, engineered cementitious materials (ECC) and interlocking masonry method. The comparison of different unique masonry strengthening methods helps us to provide a better suggestion for construction issues. In contrast to the conventional method, welded wire mesh gives better results than all other ways. Also, embedment of TRM, ECC, FRP, GFRP, CFRP, and interlocking holds an excellent performance in some other aspects. © BEIESP.
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, Mohamed
Groundwater 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.
Assessing the impact of climate and land use change on flood vulnerability: a machine learning approach in coastal region of Tamil Nadu, India
(Springer Science and Business Media Deutschland GmbH, 2025-01-27) Abijith, Devanantham; Saravanan, Subbarayan; Parthasarathy K.S.S.; Reddy, Nagireddy Masthan; Niraimathi, Janardhanam; Bindajam, Ahmed Ali; Mallick, Javed; Alharbi, Maged Muteb; Abdo, Hazem Ghassan
Flooding and other natural disasters threaten human life and property worldwide. They can cause significant damage to infrastructure and disrupt economies. Tamil Nadu coast is severely prone to flooding due to land use and climate changes. This research applies geospatial tools and machine learning to improve flood susceptibility mapping across the Tamil Nadu coast in India, using projections of Land Use and Land Cover (LULC) changes under current and future climate change scenarios. To identify flooded areas, the study utilised Google Earth Engine (GEE), Sentinel-1 data, and 12 geospatial datasets from multiple sources. A random forest algorithm was used for LULC change and flood susceptibility mapping. The LULC data are classified for the years 2000, 2010, and 2020, and from the classified data, the LULC for years 2030, 2040, and 2050 are projected for the study. Four future climate scenarios (SSP 126, 245, 370, and 585) were used for the average annual precipitation from the Coupled Model Intercomparison Project 6 (CMIP6). The results showed that the random forest model performed better in classifying LULC and identifying flood-prone areas. From the results, it has been depicted that the risk of flooding will increase across all scenarios over the period of 2000–2100, with some decadal fluctuations. A significant outcome indicates that the percentage of the area transitioning to moderate and very high flood risk consistently rises across all future projections. This study presents a viable method for flood susceptibility mapping based on different climate change scenarios and yields estimates of flood risk, which can provide valuable insights for managing flood risks. © The Author(s) 2025.
Assessment of heavy metal contamination in soil due to leachate migration from an open dumping site
(Springer Verlag, 2012-12-23) Kanmani, S; Gandhimathi, R
The concentration of heavy metals was studied in the soil samples collected around the municipal solid waste (MSW) open dumpsite, Ariyamangalam, Tiruchirappalli, Tamilnadu to understand the heavy metal contamination due to leachate migration from an open dumping site. The dump site receives approximately 400–470 tonnes of municipal solid waste. Solid waste characterization was carried out for the fresh and old municipal solid waste to know the basic composition of solid waste which is dumped in the dumping site. The heavy metal concentration in the municipal solid waste fine fraction and soil samples were analyzed. The heavy metal concentration in the collected soil sample was found in the following order: Mn > Pb > Cu > Cd. The presence of heavy metals in soil sample indicates that there is appreciable contamination of the soil by leachate migration from an open dumping site. However, these pollutants species will continuously migrated and attenuated through the soil strata and after certain period of time they might contaminate the groundwater system if there is no action to be taken to prevent this phenomenon.
Assessment of innovative dented sheet liner on the improvement of hydraulic properties of pervious concrete pile
(Elsevier B.V., 2021-05-14) Umanath, U; Muthukkumaran, Kasinathan
The Pervious Concrete Pile (PCP) is a technique that has emerged as an innovative method to improve radial consolidation and the bearing capacity of soft soils. The PCP, a stiffer vertical column (pile), has voids on the surface that accelerate the radial consolidation and eventually improve soft soils' bearing capacity. Since the PCP is stiffer material, the performance does not depend on the confined soil, unlike other drains. As the rate of radial consolidation hinges on the drain's hydraulic conductivity, ensuring the permeability of PCP along its total length is essential. An innovative method of dented sheet liner has been introduced in this study to improve the hydraulic conductivity of PCP. This method was introduced to create surface roughness and regular voids along the length of PCP. Malleable material like aluminium has been dented with specified patterns as a liner on the inner surface and introduced along with conventional formwork, which shall be removed after the concrete's final setting time. A series of laboratory experiments such as compressive strength, split tensile strength, porosity, and falling head permeability tests were performed on PCP cast using dented sheet liners. The results were compared with the conventional PCP properties to establish the efficiency of dented sheet liner in improving the hydraulic conductivity of pervious concrete. The results show that using dented sheet liners, the hydraulic properties porosity and permeability of PCP have been increased up to 22.5% and 79%, respectively, with minimum reduction in the strength parameters.
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, Aneesh
The 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.
Behavior of pile due to combined loading with lateral soil movement
(Springer, 2016-05-20) Jegatheeswaran, B; Muthukkumaran, K
Piles are commonly used to transfer vertical forces, arising primarily from super structure. Lateral loads, however, are just as important as vertical loads in designing pile foundations and are often more complicated. More powerful lateral loads occur as a result of unpredicted events such as heavy wind, earthquakes, slope failure, and lateral spread induced by liquefaction. But in actual case combined action of vertical and horizontal ground loads can occur in many situations for a pile. So the study of combined load behaviour of soil is important. But these all are suitable only in horizontal ground only. If the pile is in sloped ground then the behaviour of a pile is not only depend on the combined loading but also depends on the lateral soil movement due to the effect of slope. So in this paper by using finite element software, the behaviour of a pile due to the combined loading is studied along with and without the influence of lateral soil movement is made. The effect of the lateral soil movement is depends on the slope angle, so for this study varying slope angles (1:1, 1:1.5 and 1:2) were also considered. © 2016, Jegatheeswaran and Muthukkumaran.
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.
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.
Development of freight travel demand model with characteristics of vehicle tour activities
(Elsevier Ltd, 2020-11-01) Venkadavarahan M.; Raj, Celestin Thivya; Marisamynathan, Sankaran
The actual movements of freight vehicles demonstrate comprehensive tour activities instead of a single trip. Freight vehicle tours activity represent trip chaining relationship between shippers, receivers, and carriers as a journey of tours. In India, identifying and modeling tour activity is complex because disaggregated freight tour activity data are generally not available to access. Hence, there is a need to identify the most suitable and sufficient modeling framework, which explains the non-linear behavior and complexity of tour-based freight travel demand. Thus, the objectives of this study are to identify the driver, vehicle, and journey characteristics of freight vehicle movement and their tour activities using the conventional non-linear and soft computing methods to estimate the number of tours and to identify the most suitable and effective modeling method. The questionnaire form designed, and study locations were selected in Tiruchirappalli, India. The selected survey locations were identified based on the availability of fright drivers who were making tours. An extensive face to face data collection process was performed, and a total of 600 drivers' data were collected from survey. A detailed description of the collected data was obtained through descriptive analysis. Next, significant relationship between characteristics of freight vehicle drivers and their tour activities was identified based on various correlation tests. Further, a non-linear regression method was adopted for explaining non-linear behavior and complexity of tour-based freight travel demand. Followed by soft computing models using Support-vector machines (SVM), k-Nearest Neighbors (k-NN), and Artificial Neuron Network (ANN) algorithms were developed to predict tour-based freight travel demand, which performed better than conventional non-linear model. Among all, ANN model outperformed and explained the variations with better accuracy. Finally, the outcome of this study will be helpful to transport planners and researchers for understanding the characteristics of freight vehicle tour activity and estimating tour-based freight travel demand.
Development of urban freight trip generation models concerning establishment classification process for a developing country
(KeAi Communications Co., 2021-08-19) Venkadavarahan, Marimuthu; Marisamynathan, Sankaran
The objective of this study is to develop the Freight Trip Generation (FTG) models concerning establishment classification process for estimating freight trip activity. Establishment Based Freight Survey (EBFS) was performed to collect various supply chain variables involved in the urban freight system. Extensive data of 647 samples were collected from Tiruchirappalli city, India. Economic activity analysis shows that pure receiver and intermediate establishments do not follow the uniform freight trip activity among the industrial segments. Hence, a suitable methodology for the establishment classification process is developed using traditional and machine learning (ML) techniques to classify the establishment into an intermediate and pure receiver before estimating the freight trip activity. Supply chain variables like employment, FTA, industrial segment, mode of the commercial vehicle, type of delivery, number of suppliers, and gross floor area play a crucial role in the establishment classification process. Then, employment-based FTG models are developed with the classified data using linear and non-linear functional forms for each industrial segment. Finally, it is observed that FTG models result enhanced, when the establishment classification process is performed as an initial stage, with lower error and accurate estimation of urban freight trip activity.
Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset
(MDPI, 2023-04-27) Reddy, Nagireddy Masthan; Saravanan, Subbarayan; Almohamad, Hussein; Al Dughairi, Ahmed Abdullah; Abdo, Hazem Ghassan
Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.
Estimation of lateral capacity of rock socketed piles in layered soil-rock profile
(Springer, 2021-04-20) Prakash, A.R.; Muthukkumaran, Kasinathan
Large diameter rock socketed piles were preferred for the purpose of transmission of a huge volume of both vertical and lateral load from superstructure to a deeper depth safely without any structural defects. A series of experimental program was conducted on model pile for studying the behaviour of the rock socketed pile under static lateral load in a soil-rock layered profile system. The model piles were instrumented with displacement and force transducers for measuring the magnitude of the pile movement and load transferred by the pile. The experimental results showed that the rock socketed pile lateral capacity has significantly affected by the depth of embedment of the pile in soil and depth of rock socket. There was a considerable increase in the lateral capacity of the pile when the depth of socketing is three times the diameter of the pile into rock with a minimum embedment. In the 3D socketed piles, the lateral capacity of the pile is almost 18 times higher than the non-socketed piles. From the experimental study, it is also observed that when the piles socketed more in to the hard strata (rock), the depth of fixity increases and the lateral displacement reduces substantially. © 2021, The Author(s).
Evaluation of hydrological responses to decadal variability of land use/land cover patterns in tropical river basin of the Western Ghats, Southern India
(Taylor and Francis Ltd., 2026-07-03) Gayen, Sudeshna; Seenipandi, Kaliraj; Srinivas, Reji; Devaraj, Suresh; Subbarayan, Saravanan; Loganathan, Parthiban
LULC change is increasingly altering hydrological processes in the Karamana River Basin, which faces declining vegetation cover, increasing impervious surfaces, and growing drought vulnerability. This study evaluates decadal LULC transformations and their hydrological impacts using Landsat (2000, 2015 and 2025), MODIS-derived variables, and IMD rainfall data integrated within a GIS-SVM framework. Unlike previous studies that primarily assessed individual indicators, this research provides a comprehensive evaluation linking LULC dynamics with multiple hydrological and drought indicators. Built-up areas expanded by 68.2% between 2000 and 2025, accompanied by a 2.12 °C increase in LST. NDVI maximum values declined from 1 to 0.92, NDWI decreased from 0.79 to 0.48, ET reduced from 55.88 to 38.60 mm, and SPI ranged from −1.84–0.70 to −1.47–1.13, indicating localized severe drought hotspots (SPI < −1.5). The findings highlight the need for sustainable land-use planning, urban growth regulation, watershed restoration, and climate-resilient water resource management. © 2026 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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.
Exploring spatial machine learning techniques for improving land surface temperature prediction
(Elsevier B.V., 2024-05-05) Arunab, K.S.; Mathew, Aneesh
Land 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.

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