Browsing by Author "Kundapura, S."

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A NDVI Based Approach To Detect The Landslides By Using Google Earth Engine
(Institute of Electrical and Electronics Engineers Inc., 2023) Vishnu Vardhan, M.; Harish Kumar, S.; Mohan Kumar, S.; Kundapura, S.
Detection of landslide-prone areas plays an important role in planning urban connectivity like roads, bridges, etc. Landslides are generally caused by a variety of factors, the most important of which is rainfall. In this paper, the detection is carried out in four taluks of Chikkamagaluru district, namely Koppa, Sringeri, Mudigere, and Narashimarajpur; these four taluks are located in the Western Ghat region. Landslides are primarily caused by heavy rainfall during the monsoon season. For the detection of landslides, Sentinel optical and SAR data are used because of their 10metre resolution and revisiting period of two to five days. The entire methodology for detecting landslides is carried out in Google Earth Engine due to its large collection of data, which aids in multi-temporal studies. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in the detection of landslides. For the detection of landslides, Normalized Difference Vegetation Index (NDVI) is used for Sentinel-2 data and the SAR backscatter change approach is used for Sentinel-1 images, and I thresholding is applied to both methods to detect areas where landslides had occurred. The main thing is that no previous landslide inventory data is used for detection. The previous landslide inventory is used for validation purposes only. Finally, the performance of both approaches was compared using accuracy assessment properties such as overall accuracy and kappa coefficient to determine which approach is superior. Â© 2023 IEEE.
A Non-stationary Hydrologic Drought Index Using Large-Scale Climate Indices as Covariates
(Springer Science and Business Media Deutschland GmbH, 2023) Sajeev, A.; Kundapura, S.
The dry and wet periods can be analyzed based on different drought indices. Most existing drought studies are based on stationary assumptions, and environmental changes are not considered. This study proposes a non-stationary streamflow-based drought index, incorporating large-scale climate indices to study hydrological drought for 45Â years. Climate indices are used as covariates for building the non-stationary model fitted to streamflow. Correlation analysis is carried out to determine the best covariates for the streamflow in the Netravati River basin in India. The Southern Oscillation Index (SOI) exhibited a significant influence on streamflow at all time scales. The non-stationary model is compared with the stationary model, and the best model is chosen based on the Akaike information criterion (AIC). Under statistical measures, non-stationary models performed better than stationary ones at all time scales. The generalized additive model for location, scale, and shape (GAMLSS) is used for non-stationary modeling. The models are developed for short-term (3 and 6Â months) and long-term (12 and 24Â months) droughts. The influence of climate variables on drought classes is analyzed, and more severe drought is observed under the non-stationary scenario. The deficiency in streamflow was more than 60% in the basin in 1987 and 2002. The non-stationary drought index detected more severe drought events than the stationary index under short-term scales. Hydrological drought properties such as drought severity, duration, and peak are calculated under stationary and non-stationary scenarios, and a noticeable difference is observed. Compared to stationary models, the non-stationary model yields more logical and satisfactory findings because it effectively takes into account non-stationarities in the streamflow caused by climate change. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
A statistical approach for comparison of secondary precipitation products
(Springer Science and Business Media Deutschland GmbH, 2021) Kommu, R.; Kundapura, S.; Venkatesh, V.
Meteorological data retrieval is the fundamental process for any hydrological research. Precipitation data collection from some constrained territories like high slant geography and inaccessible areas is exceptionally troublesome. Setting the rain gauges is a matter of expense and timely maintenance. To overcome these issues, satellite sensors producing high spatial and temporal resolution datasets can be utilized in the studies involving precipitation component. These satellite products are affected by biases, and hence, there is a need for calibration and verification by using ground observation data based on the statistical coefficients. In this study, the most accessible satellite data products, i.e., CHIRPS, PERSIANN-CDR and TRMM, are employed to check the accuracies against IMD gridded data for the years 2000â€“2012 using a statistical approach. Selecting the data product having a high coefficient of correlation and low PBIAS is utmost necessary. The current study was performed based on catchment-to-catchment (C-C) method by comparing IMD gridded data with satellite datasets obtained from Google Earth Engine. The results can highlight the data product which can conquer the issue of data inaccessibility in the investigation territory and can be utilized as reference precipitation dataset for different hydrological applications. Â© Springer Nature Singapore Pte Ltd 2021.
AN Integrated Analysis and Forecasting of Wildfires in the Nallamala Hills, India
(Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Vishnu Vardhan, M.; Apoorva, K.V.
Wildfires threaten ecosystems, human lives, and infrastructure, necessitating effective detection and prediction methods. In this study, an in-depth analysis of wildfire detection and forecasting is carried out over the Nallamala hills, which stretch across the states of Telangana and Andhra Pradesh. Our approach comprises three significant steps: Active fire analysis, pre-fire analysis, and post-fire analysis. Pre-fire maps were created using the Normalised Difference Vegetation Index (NDVI) during the pre-fire analysis, which involved time series analysis of significant components. For active fire analysis, the first dataset is created by using satellite imagery and its derived products. A dataset is used to train the five different machine-learning models for prediction. Among these models, the Random Forest classifier outperformed the remaining four models (Support vector Classifier, Gradient Boosting Classifier, Logistic Regression, and K-means algorithms) in accurately detecting and predicting active fires. This step enabled real-Time monitoring and prioritisation of firefighting efforts. The burnt area calculation uses the Normalised Burn Ratio (NBR) in the post-fire analysis. The analysis implemented post-fire rehabilitation and restoration efforts, giving essential information on the scope and severity of fire damage. The comprehensive study of all wildfires will provide a detailed picture of what occurred in the past (Timeseries), present (Prediction models), and future (Pre-fire maps), allowing people and government agencies to take precautions against future wildfires. Â© 2023 IEEE.
Assessing the Impacts of Land Use, Land Cover, and Climate Change on the Hydrological Regime of a Humid Tropical Basin
(American Society of Civil Engineers (ASCE), 2023) Abraham, A.; Kundapura, S.
Climate change and land use land cover (LULC) change are two major factors influencing river basin hydrology. This study explored these drivers' isolated and combined impacts on the ecologically relevant flow in the Achencoil basin, Kerala, India. The LULC classification in the study is carried out with the Random Forest (RF) algorithm in the Google Earth Engine (GEE) platform, and Land Change Modeler (LCM) is incorporated for change detection and projection. The future climate data from the National Aeronautics and Space Administration Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) is used for climate change impact assessment. The Soil and Water Assessment Tool (SWAT) is employed to simulate streamflow under LULC and climate change scenarios. The historical and projected future LULC change in the basin revealed an increase in the built-up and barren land, with a significant decrease in agricultural and forest areas. The results show that the projected future precipitation will decrease under the RCP 4.5 and increase under the RCP 8.5 scenario. The projected average maximum and minimum temperature are expected to increase under both scenarios in the basin. The LULC 2050 scenario shows the most significant rise in average annual streamflow, at 7.5%. Whereas in the climate change scenarios, the average annual flow decreases under RCP 4.5 and increases under RCP 8.5. The combined impacts of climate change and LULC change are relatively higher than the isolated effects of these drivers in the basin. The study outcomes are expected to help policymakers consider the effect of climate change and LULC change on the river's hydrology so as to implement the management activities that account for the riverine ecosystem. © 2023 American Society of Civil Engineers.
Assessment of Changes inWetland Storage in Gurupura River Basin of Karnataka, India, Using Remote Sensing and GIS Techniques
(Springer Science+Business Media, 2018) Kundapura, S.; Kommoju, R.; Verma, I.
In view of the significant importance of wetlands in the ecosystem and regional economy, an attempt has been made to analyze the impact of land use/land cover dynamics and other contributing factors on spatial status of Gurupura river basin wetland ecosystem located in Karnataka region. The impact assessment has been carried out by analyzing the multi-temporal changes in the storage capacities of wetlands in the watershed, by using remote sensing data of LISS-III. The multi-temporal land use/land cover statistics will reveal the significant changes that have taken place over time in the watershed. The runoff generated can be easily calculated from this information which gives an idea of the total input into the system. In response to these upstream watershed changes, wetland has exhibited changes in spatial extension, structure, and hydrological characteristics. As a consequence of continuously changing land use/land cover characteristics and unpredictability of the monsoon, the wet land ecosystems have exhibited considerable changes in spatial extent and their storage capacities. Overall, there has been degradation in the storage capacities of the wetland ecosystems of the region causing a multitude of adverse effects such as increase in floods and submergence of mainland. © Springer Nature Singapore Pte Ltd. 2019.
Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater
(2019) Kundapura, S.; Arkal, V.H.; Pinho, J.L.S.
Coastal defenses such as the breakwaters are important structures to maintain the navigation conditions in a harbor. The estimation of their hydrodynamic characteristics is conventionally done using physical models, subjecting to higher costs and prolonged procedures. Soft computing methods prove to be useful tools, in cases where the data availability from physical models is limited. The present paper employs adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models to the data obtained from physical model studies to develop a novel methodology to predict the reflection coefficient (Kr) of seaside perforated semicircular breakwaters under low wave heights, for which no physical model data is available. The prediction was done using the input parameters viz., incident wave height (Hi), wave period (T), center-to-center spacing of perforations (S), diameter of perforations (D), radius of semicircular caisson (R), water depth (d), and semicircular breakwater structure height (hs). The study shows the prediction below the available data range of wave heights is possible by ANFIS and ANN models. However, the ANFIS performed better with R2 = 0.9775 and the error reduced in comparison with the ANN model with R2 = 0.9751. Study includes conventional data segregation and prediction using ANN and ANFIS. 2019, Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature.
Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater
(Harbin Engineering University, 2019) Kundapura, S.; Arkal, V.H.; Pinho, J.L.S.
Coastal defenses such as the breakwaters are important structures to maintain the navigation conditions in a harbor. The estimation of their hydrodynamic characteristics is conventionally done using physical models, subjecting to higher costs and prolonged procedures. Soft computing methods prove to be useful tools, in cases where the data availability from physical models is limited. The present paper employs adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models to the data obtained from physical model studies to develop a novel methodology to predict the reflection coefficient (Kr) of seaside perforated semicircular breakwaters under low wave heights, for which no physical model data is available. The prediction was done using the input parameters viz., incident wave height (Hi), wave period (T), center-to-center spacing of perforations (S), diameter of perforations (D), radius of semicircular caisson (R), water depth (d), and semicircular breakwater structure height (hs). The study shows the prediction below the available data range of wave heights is possible by ANFIS and ANN models. However, the ANFIS performed better with R2 = 0.9775 and the error reduced in comparison with the ANN model with R2 = 0.9751. Study includes conventional data segregation and prediction using ANN and ANFIS. © 2019, Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature.
Beyond the data range approach to soft compute the reflection coefficient for emerged perforated semicircular breakwater
(Springer, 2019) Kundapura, S.; Hegde, A.V.; Wazerkar, A.V.
Prediction of reflection coefficient (Kr) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (Kr) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (Hi, T, S, D, R, d, hs) that influence the reflection coefficient (Kr) are considered for training as well as testing, where Hi is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and hs is the structure height. The accuracy of predictions of reflection coefficient (Kr) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient Kr of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of Kr in the case of beyond the data range segregation method. © Springer Nature Singapore Pte Ltd. 2019.
Characterization of the Adhesive Layer through Finite Element Modeling in Double Strap Joints and Validation Through Digital Image Correlation
(Springer, 2025) Sahana, T.S.; Kaliveeran, V.; Raveesh, R.M.; Kundapura, S.
The accurate measurement of strain on engineering structures is crucial for evaluating their performance and ensuring their structural integrity. Strain gauges are employed for this purpose, enabling to monitor mechanical deformations and stress distributions. The adhesive layer serves as a medium between the strain gauge and the material being tested. Changes in the adhesive layer can impact, and strain transfers between materials and strain gauges when subjected to different types of loading. The p study investigates the adhesive layer's role in strain gauge mounting on substrates through experiments, finite element analysis (FEA) and comparison of analytical model predictions (Volkersen and Tsai) with the experimental digital image correlation (DIC) results. Experiments are carried out for double strap joint samples and three-dimensional finite element analysis is carried out with aid of ANSYS software. FEA model is used to simulate the adhesive layer's mechanical behavior, taking into account material properties and boundary conditions determined through experimental characterization. DIC data are used to understand the strain transfer mechanism of the adhesive layer in strain gauge mounting. The findings from both the FEA and experimental studies highlight the significance of the adhesive layer's properties, in obtaining precise strain measurements and strain transfer mechanisms. A thorough comparison of FEA predictions with experimental results allows for identifying critical factors that influence the accuracy of strain gauge measurements using DIC adhesive layers. This study offers guidance for choosing suitable adhesive materials and ideal mounting configurations for particular application. © ASM International 2024.
Comparative evaluation of meteorological and hydrological drought using stationary and non-stationary indices in a semi-arid river basin in India
(Springer Science and Business Media B.V., 2024) Sajeev, A.; Kundapura, S.
Few researchers have incorporated climate change in drought indices calculations and conducted comparative analyses of meteorological and hydrological droughts using non-stationary indices. The primary objective of this research is to develop non-stationary indices for assessing meteorological and hydrological droughts in the Shetrunji River basin in India. The climate oscillations are used as covariates to create non-stationary models by applying the generalized additive model in location, scale, and shape from 1971 to 2015. The statistical performance of stationary and non-stationary models has been compared across various time scales (3-, 6-, 12- and 24-months), and the results indicate that non-stationary models more effectively capture meteorological and hydrological drought events than stationary models. The drought and flood events detected by non-stationary indices are compared with historical episodes to assess the robustness of the indices. The results are also compared with drought events obtained from rainfall and streamflow departures. The annual and seasonal departures in rainfall and streamflow show the highest deficiency of rainfall and streamflow in 1987. The probability of different drought classes is calculated, and a higher likelihood of severe to extreme dry conditions is observed compared to very wet and extreme wet conditions in the basin. Investigation has been conducted on the impact of meteorological drought on hydrological drought and a correlation analysis between both types of drought. A significant correlation is observed between meteorological and hydrological drought at all analyzed time scales. Meteorological drought impacts surface water resources with a one-month lag at all time scales, with the highest response rate obtained at 6-month scale (91.13%). The study also examines the impact of drought on yield loss in kharif (bajra) and rabi (wheat) crops. Bajra and wheat yield loss rates strongly correlate with non-stationary drought indices, with a more significant effect of drought on bajra yield than wheat during major drought events. This novel dimension of drought studies provides practical insights into semi-arid regions in a changing environment. The findings can be utilized by various sectors, including drought management, agricultural planners, and policymakers, to reduce crop loss due to drought. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Current approaches of artificial intelligence in breakwaters - A review
(Techno Press technop2@chollian.net, 2017) Kundapura, S.; Hegde, A.V.
A breakwater has always been an ideal option to prevent shoreline erosion due to wave action as well as to maintain the tranquility in the lagoon area. The effects of the impinging wave on the structure could be analyzed and evaluated by several physical and numerical methods. An alternate approach to the numerical methods in the prediction of performance of a breakwater is Artificial Intelligence (AI) tools. In the recent decade many researchers have implemented several Artificial Intelligence (AI) tools in the prediction of performance, stability number and scour of breakwaters. This paper is a comprehensive review which serves as a guide to the current state of the art knowledge in application of soft computing techniques in breakwaters. This study aims to provide a detailed review of different soft computing techniques used in the prediction of performance of different breakwaters considering various combinations of input and response variables. © 2017 Techno-Press, Ltd.
Deriving water level and discharge estimation using satellite altimetry for Krishna River, Karnataka
(Elsevier B.V., 2021) Garkoti, A.; Kundapura, S.
Radar altimetry is the most commonly used approach for monitoring ocean's water level or inland water bodies over ungauged or poorly gauged regions. In this study, a network of three altimetry missions (i.e., Jason 3, Sentinel 3A, and Sentinel 3B) was traced in the Krishna river, Karnataka, India, for 2018 and 2019. The network consists of 13 virtual stations, of which two are of Jason 3, five are of Sentinel 3A, and six are of Sentinel 3B. This paper proposes a method to find river discharge solely using remote sensing derived data. The river depth is calculated using satellite altimetry, and other hydraulic parameters (river width and river bed slope) are used in modified Manning's equation for calculation of discharge. To estimate river width, Google Earth Engine is used to process Sentinel 1 and Sentinel 2 images. All three missions showed excellent results with an average RMSE of 1.43 m, and the best correlation is showed by Sentinel 3B (>0.97). The discharge calculated using this method also provides adequate results, with NS value for station 1 is 0.53, and for station 2 is 0.63. These results show the potential of the proposed approach for monitoring water level and estimation of discharge solely using satellite-derived products. © 2021 Elsevier B.V.
Design and Fabrication of Block Stiffened Frame
(The Aeronautical and Astronautical Society of the Republic of China, 2025) Bangaru, P.; Kaliveeran, V.; Raveesh, R.M.; Palanikumar, P.; Kundapura, S.
This research introduces a block-stiffened frame for accurate load measurements by reinforcing SS304 rigid blocks along SS304 thick strips. The frame achieves variable stiffness without recalibration, making it more adaptable. Rectangular rosettes are mounted along the longitudinal direction of the frame to measure transverse or tangential loads. Stress analysis is carried out using both finite element analysis (FEA) and experimental methods with both essential and non-essential boundary conditions. Specific locations on the outer surfaces of the block-stiffened frame are examined to compare stress results from finite element analysis and experiments, which confirms a strong agreement between the two methods. The results demonstrate that the frame remains stable under repeated loadings, making it suitable for multiple applications, especially in aerospace structures. Unlike conventional stiffened beams, this novel design easily adjusts stiffness, making load calibration flexible, efficient and adaptable. © 2025 The Aeronautical and Astronautical Society of the Republic of China. All rights reserved.
Development and Performance Evaluation of a Block-Stiffened Fretting Fixture for Accurate Load Transfer in Fretting Tests
(Springer Science and Business Media Deutschland GmbH, 2025) Bangaru, P.; Kaliveeran, V.; Raveesh, R.M.; Kundapura, S.
This study introduces a novel fretting fixture designed for fretting and full sliding tests, which minimizes recalibration and integrates multiple sensors for measuring contact tractions. The fixture features an additional “Z”-type supporting structure that enables horizontal chassis length adjustments and is compatible with various fatigue testing machines. Adjustable components, such as vertical stiffeners reinforced with rigid blocks, facilitate achieving the target Load Transfer Ratio (LTR) of 50%. The LTR can be adjusted by repositioning the rigid blocks using a bolt-nut assembly. The fretting tests are conducted with and without blocks attached to the vertical stiffeners. Finite Element Analysis (FEA) and experimental methods are employed to ealuate the LTR, yielding results of 59–60% and 58–59%, respectively. Supported by FEA and experimental validation, the fixture demonstrates effectiveness and reliability for fretting studies. The stiffened fretting fixture is evaluated under higher normal loads and increased frequencies, showing a notable increase in the Q/P ratio. This increase highlights intensified surface interaction and pronounced tribological effects. © The Society for Experimental Mechanics, Inc 2025.
Experimental and Numerical Studies on the Stiffening of Tubular T-joint of Offshore Jacket Structures
(Springer Science and Business Media Deutschland GmbH, 2024) Murugan, N.; Kaliveeran, V.; Raveesh, R.M.; Kundapura, S.
Present study investigates the stiffening effect on the behavior of tubular T-joints in offshore platform jacket structures subjected to axial compression. Stiffening is crucial to enhance the structures' strength and lifetime. Tubular cross section structures are preferred due to their mechanical properties and cost-efficiency. The study introduces an innovative technique by adding stiffeners at the interface between braces and chords to effectively distribute loads from multiple directions. The T-joint specimen used has specific dimensions: Chord length 400 mm, brace length 200 mm, chord diameter 100 mm, brace diameter 50 mm, chord thickness 4 mm, and brace thickness 3 mm. Experimental tests and Numerical simulations were conducted to measure failure loads for both stiffened and unstiffened T-joints. Stiffened configurations (4, 6, and 8 strips) has a notable impact on the ultimate capacity of the T-joint, showcasing an increase in strength compared to the unstiffened joint. Stiffened joints showed a significant increase in ultimate strength compared to unstiffened joints, with improvements ranging from 67.18 to 73.33% for different stiffener configurations. Joint local stiffness also improved substantially, with percentage increases ranging from 67.03 to 140.80% for various stiffener configurations. Present research work demonstrates the positive impact of stiffeners on tubular T-joints, improving their strength and stiffness while showing strong agreement between numerical simulations and experimental results and the study also concludes that the addition of stiffeners effectively enhances the ultimate capacity and local stiffness of tubular T-joints. These findings emphasize the effectiveness of the proposed reinforcement strategies for optimizing tubular T-joints in offshore structures. © The Author(s), under exclusive licence to Shiraz University 2023.
Experimental Investigation of the Behavior of Tubular T-Joint of Jacket Structures
(Springer, 2024) Murugan, N.; Kaliveeran, V.; Kundapura, S.
This study deals with a preliminary experimental study to examine the behavior of tubular T-joint of Jacket structures under compressive load, which is helpful for further study of reinforcement in T-joints for strengthening. A specimen of T-joint with geometric dimensions of chord length = 494 mm, chord diameter = 141 mm, chord thickness = 5 mm, and brace length = 237 mm, brace diameter = 90 mm, brace thickness = 4.5 mm was considered for this study. The specimen is subjected to axial compressive load which is applied from the top end of the brace member. The ends of the chord member are in simply supported condition. The experiment is conducted in a 40 T UTM machine. The loads are applied with an interval of 50 kgf starting from zero to the yield load of 9,600 kgf. The experimental setup, specimen details, and the relevant results (load-deformation relationship and failure mechanism) are presented. The findings of the study, i.e., local joint deformation behavior under compressive load, are presented graphically. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Feature Elimination and Comparative Assessment of Machine Learning Algorithms for Flood Susceptibility Mapping in Kerala, India
(Institute of Electrical and Electronics Engineers Inc., 2023) Kundapura, S.; Aditya, B.; Apoorva, K.V.
Floods are a catastrophic phenomenon with far-reaching consequences for infrastructure, the economy, and human lives, profoundly impacting regions globally. This study assesses flood susceptibility in four districts of Kerala: Ernakulam, Idukki, Kottayam, and Alappuzha. For the 2018 storm that caused flooding by Cyclone Ockhi, a flood map for the area was produced using Sentinel 1 satellite data in Google Earth Engine environment. The resulting map served as the foundation for further analysis. Based on the literature review, 16 potential flood causative factors were identified and incorporated into spatial maps in the Geographic Information System (GIS) environment. Analysis of the flood dataset was performed using Machine Learning (ML) algorithms, namely, Random Forest (RF), Decision Tree (DT), Gradient Boosting Machine (GBM), and XG Boost (XGB). Grid search was employed to identify the optimal hyperparameters for each algorithm, ensuring improved performance. Recursive Feature Elimination (RFE) was subsequently applied to select the most influential variables, resulting in a refined dataset. The chosen factors' feature importance scores were obtained, which were used to create the flood susceptibility map using the four ML models in a GIS environment. Evaluation metrics such as F1 score, accuracy, precision, recall, and ROC-AUC score were computed for each model, providing insights into the effectiveness of each algorithm in predicting the flood occurrence. The resulting flood susceptibility map for the best-performing ML model visually represents the varying levels of flood risk in the study area. Â© 2023 IEEE.
Finite Element Modelling and Experimental Validation of Strain Gauge Pasted Over the Surface of a Substrate Subjected to a Transverse Load
(Springer, 2024) Raveesh, R.M.; Kaliveeran, V.; Kundapura, S.
The strain measurement is important as it directly involves with the deformation of a structure in the field of engineering. Strain is a measure of change in shape that occurs when an external load is applied to an engineering assembly. The evaluation of the strain is used to determine the amount of extension or deformation a structure experiences under different loading conditions. Strain gauges are electrical resistance sensors bonded at critical locations on the surface of structural components to detect surface deformation. Strain gauges are frequently used to continuously check for deformations to avoid accidents that can occur in nuclear power plants, aerospace vehicles, mechanical components, and structures. Strain gauges applied directly to the specimen are partially affected by the bonding material and thickness when tested. Present work intends to study the effect of adhesive thickness on strain values. Adhesives are used to paste strain gauges over the surface of the specimen. Three-Dimensional analysis of the strain gauge model has been carried out with the aid of the Finite element software. Experiments were conducted to study the effect of adhesive thickness by varying the thickness of the adhesive from 0.1 to 1 mm by pasting strain gauge over the surface of the Aluminium specimen of length 230 mm, width of 30 mm, and thickness of 6 mm. The strain values obtained from the finite element analysis were compared with the strain values obtained from the experiments. Finite element analysis results were found to be in good correlation with the experimental results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Flood inundation mapping of harangi river basin, kodagu, using gis techniques and hec-ras model
(Springer Science and Business Media Deutschland GmbH, 2021) Dev Anand, M.R.; Kundapura, S.
Flood is the most common hydrologic event frequently experienced in India. The states of Kerala, UP, West Bengal, Karnataka and Assam were the mainly affected by flood in 2018. In Kodagu, the southern district of Karnataka, many people have been affected by heavy rains. Landslides in hilly terrain and flooding have worsened the lives of people and led to the destruction of 800 homes, 240 bridges collapsed, road networks of 2225Â km damaged and 65 government buildings affected. The cost of rebuilding road infrastructure and buildings is approximately Rs. 3000 crores. While developing flood mitigation measures, flood inundation maps are an essential component, which will be useful for the planning stage. The mapping is expected to estimate the prone flood zone based on river flood stage without performing additional simulations and quantification of the flood risk with respect to different vulnerability parameters giving a clear picture of the planning stage. These are going to be achieved by both 1D hydrodynamic models and GIS environment. This study gives an insight about how unscientific development activities may increase the negative impacts of natural disasters. It can support the planners to correctly identify the non-vulnerable places while rebuilding the damaged infrastructure. This can help people to resettle permanently in a safer place, so that they will not be affected in the case of future disasters. Depending on the severity of the water levels, we can identify the area for construction of hydraulic structures for flood protection. Â© Springer Nature Singapore Pte Ltd 2021.