Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
Browse
18 results
Search Results
Item 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.Item 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.Item Urban Weighted Green Index- A study of urban green space in relation to Land Surface Temperature for Lucknow city, India(Elsevier B.V., 2020) Verma, R.; Kundapura, S.It is of substantial importance, that the green spaces with regards to the urban areas are quantified in a manner that a planned urban area is developed and utilized with best access to vegetation. There exist various factors which affect built-up areas and corresponding neighbourhood like Vegetation Density (VD), Proximity to high vegetation areas (VDP), Built-up Density (BD), Rise of built-up (HT), and Proximity to high built-up density (BDP). In this study using ©JAXA AW3D DSM and above mentioned factors, Urban Weighted Green Index (UWGI) is generated. To understand the UHI effect, Land Surface Temperature (LST) is generated using radiative transfer equation, which is used for portraying relationship between LST and UWGI for 33 zones and different land use (LU) and land cover (LC) classes of Lucknow Development Authority area. Interdependent effect of all factors was compared and it is seen that VD and VP to different rise areas is affecting areas more than RD and RP, in terms of UWGI and LST both. The increase of one unit in UWGI is affecting the decrease of 2.5 ?C in various LU classes of Lucknow city. © 2020 Elsevier B.V.Item PSO-ANFIS hybrid approach for prediction of wave reflection coefficient for semicircular breakwater(Taylor and Francis Ltd., 2021) Kundapura, S.; Hegde, A.V.Breakwaters are used to provide protection to the coast and are being improved over the years through research. Semicircular breakwater (SBW) is one such contribution in the area of coastal structures with an improved esthetics and stability. Advances in artificial intelligence applications in several fields have led to the increased interest in the researchers of coastal engineering to venture into it. This paper focuses on the prediction of reflection coefficient (Kr) for SBW using adaptive neuro-fuzzy inference system (ANFIS) and a hybrid of particle swarm optimization for adaptive neuro-fuzzy inference system (PSO-ANFIS) for a wide range of wave heights. The datasets required for the study are acquired from the experimental investigations of SBW in the regular wave flume at the Marine Structure Laboratory, National Institute of Technology Karnataka, India. The data fed for training and testing were taken in two forms separately, i.e. dimensional and dimensionless form. The PSO-ANFIS based optimized prediction of reflection coefficient is compared with the prediction arrived through ANFIS-based learning. The accuracy assessment of prediction was done by correlation coefficient, scatter index, Nash–Sutcliffe efficiency, bias, and root mean square error. The PSO-ANFIS hybrid model prediction improved the ANFIS prediction for the considered cases. © 2018 Indian Society for Hydraulics.Item 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.Item Spatio-temporal Dynamics of Land Use Land Cover Changes and Future Prediction Using Geospatial Techniques(Springer, 2022) Abraham, A.; Kundapura, S.Land use land cover (LULC) plays a key role in earth surface processes, and it is important to understand the spatio-temporal dynamics of LULC in an area. The study is carried out in the Meenachil and Manimala basins in Kerala, India, using land change modeller (LCM) to predict future LULC. The Random Forest (RF) classifier is used to classify the LULC in Google Earth Engine (GEE) for the years 1990, 2000, 2008, 2018 and 2021. The overall accuracy obtained for the years 1990, 2000, 2008, 2018 and 2021 is 92.53%, 91.42%, 96.92%, 87.79% and 95.54%, respectively, followed by a Kappa coefficient of 90.67%, 89.27%, 96.12%, 84.55% and 94.39%. LCM is utilised for LULC change detection, the model is validated successfully in predicting the LULC distribution in 2021, and the results were compared with the actual 2021 LULC. The results revealed the expansion of the built-up area and the decline of the agriculture class in these basins. The study then utilised LCM to predict future LULC up to the year 2050 at decadal intervals. The predicted future LULC maps revealed the drastic expansion of built-up; these basins might witness in the coming decades. The built area from 1990 to 2050 is expected to increase to 100.88 km2 and 60.75 km2 in Meenachil and Manimala basins, respectively. The agriculture area showed a decrease from 861.7 to 728.29 km2 in Meenachil and 743.5–676.89 km2 in Manimala basin. The outcome of the study showed the transformation of the considered land cover classes due to developmental activities in the region. The outcomes of the study can be considered as suitable inputs to land use planners for effective land use planning and management. © 2022, Indian Society of Remote Sensing.Item Recent Changes in Hydrometeorological Extremes in the Bilate River Basin of Rift Valley, Ethiopia(American Society of Civil Engineers (ASCE), 2023) Lambe, B.T.; Kundapura, S.The hydroclimatic extremes such as floods and droughts have been causing damage and losses with rising frequency than ever before. The human-induced and internal climate variability create extreme events and local hydrometeorological changes influencing climate-sensitive sectors. This research is aimed at analyzing the recent changes in the hydrometeorological extremes using indices over the Bilate basin in Ethiopia. Mann-Kendall and Sen's slope estimator were used to examine changes in hydrometeorological extreme indices. The rainy days' rate of change falls between þ10.64 mm in the downstream to −10.67 mm in the upstream north. The wet day rainfall and heavy rainfall day indices were stronger in the basin's southwest, implying more likely flood events. The consecutive dry days show a rising tendency with more variability, while the consecutive wet days show no trend with less variability. The change point analysis revealed inconsistencies for the majority of the extreme indices. The stations' average warmest nights and days significantly increased at a rate of 0.0358°C and 0.0320°C per annum, respectively. The coldest nights in most of the stations show a significant and negligible rise in the basin while on the coldest days more than half of the stations declining. The peak flow in the annual and seasonal time series shows a rising trend and a dominant rise in most low flow indices, which possibly flashes downstream flooding. The global and local climate anomalies revealed a weak correlation, but with overlap of wet and drought years. Basin water resource plans may benefit from identified overlap cross of threshold years for improved flood control and drought monitoring. © 2018 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Item Spatiotemporal variation in the water quality of Vembanad Lake, Kerala, India: a remote sensing approach(Springer Science and Business Media Deutschland GmbH, 2023) Kulithalai Shiyam Sundar, K.S.S.; Kundapura, S.Water quality is one of the essential parameters of environmental monitoring; even a slight variation in its characteristics may significantly influence the ecosystem. The water quality of Vembanad Lake is affected by anthropogenic effects such as industrial effluents and tourism. The optical parameters representing water quality, such as diffuse attenuation (Kd), turbidity, suspended particulate matter (SPM), and chlorophyll-a (Chl-a), are considered in this study to evaluate the water quality of Vembanad Lake, Kerala, India. As this lake is regarded as of ecological importance by the Ramsar Convention and has faced severe concerns over recent years, there was a substantial change in the water quality during the lockdowns of the COVID-19 pandemic. This research is aimed at examining the change in water quality using optical data from Sentinel-2 satellites in the ACOLITE processing software from 2016 to 2021. The analyses showed a 2.5% decrease in the values of Kd, whereas SPM and turbidity show a reduction of about 4.3% from the year 2016 to 2021. The flood and the COVID lockdown had an impact on the improvement in the quality of water from 2018 to 2021. The findings indicated that the reduction in industrial activities and tourism had a more significant effect on the improvement in the water quality of the lake. There was no substantial change in the Chl-a until 2020, whereas an average decrease of 12% in Chl-a values was observed throughout 2021. This decrease can be attributed to the reduction in the lake’s hydrological residence time (HRT). Thus, these findings will be a valuable reference to help the government and non-government organizations (NGO) during strategic planning. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Spatial Mapping of Flood Susceptibility Using Decision Tree-Based Machine Learning Models for the Vembanad Lake System in Kerala, India(American Society of Civil Engineers (ASCE), 2023) Kulithalai Shiyam Sundar, P.; Kundapura, S.Floods have claimed the lives of countless people and caused significant property damage in many countries, putting their livelihoods in the jeopardy. The Vembanad lake system (VLS) in Kerala, India, has faced adverse mishappening during 2018, 2019, and 2021 floods in the state due to torrential rainfall. The goal of this research is to construct effective decision tree-based machine learning models such as adaptive boosting (AdaBoost), random forest (RF), gradient boosting machines (GBMs), and extreme gradient boosting (XGBoost) for integrating data, processing, and generating flood susceptibility maps. There are 18 conditioning parameters considered, which include seven categories and 11 numerical data. These seven categorical data were converted to numerical data, bringing the total amount of input data to 61. The recursive feature elimination (RFE) was utilized as the feature selection technique, and a total of 22 layers were chosen to feed into the machine learning models to generate the flood susceptibility maps. The efficiencies of the models were evaluated using receiver operating characteristic (ROC)-area under the ROC curve (AUC), F1 score, accuracy, and kappa. According to the results, the performance of all four models demonstrated their practical application; however, XGBoost fared well in terms of the model's metrics. For the testing data set, the ROC-AUC values of XGBoost, GBM, and AdaBoost are 0.90, whereas it was 0.89 for RF. The accuracy varied significantly among the four models, with XGBoost scoring 0.92, followed by GBM (0.88), RF (0.87), and AdaBoost (0.87). As a result, this map may be utilized for early mitigation actions during future floods, as well as for land-use planners and emergency managers, assisting in the reduction of flood risk in regions prone to this hazard. © 2023 American Society of Civil Engineers.Item 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.