Rainfall-based flood prediction by hybrid deep architecture with entropy and statistical feature set Vanam Yoganand, Sheela Rani B, Nagamani Kattukota, Eppakayala Balakrishna, Mohammad Suhail Meer International Journal of Image and Data Fusion, 2025 Flooding is a natural disaster with devastating socio-economic and environmental impacts globally. The existing methods rely on the incomplete or sparse historical data, leading to less reliable predictions. Also, deals with a narrow set of features, missing out on critical aspects of rainfall and hydrological dynamics which affects its generalisability. To address these challenges, this paper presents a novel approach, the Hybrid Deep Architecture with Entropy-based Flood Prediction (HDAE-FP) model for rainfall-based flood prediction. The process begins with data acquisition, followed by preprocessing to normalise the data by using ANAMM scaling Normalization for uniformity across features. Feature extraction entails computing statistical features such as mean, median, standard deviation, variance, skewness, kurtosis, and percentiles, alongside calculating MKE-based entropy measures to capture rainfall pattern uncertainty and hydrological dynamics complexity. These extracted features serve as input variables for prediction models. For the rainfall and flood prediction, a hybrid architecture is adopted that combines both Modified Attention-based Long Short-Term Memory (MA-LSTM) and Bi-GRU. As a result, the proposed MA-LSTM+Bi-GRU model has attained an accuracy of 0.957 at the 90% of training which surpasses the outcomes of the prediction compared to other current techniques.
Analysis and Automation of Pipe leakage deduction using Artificial intelligence and machine learning T.UdayaBanu, B.Anuradha, M. Radha, S. Packialakshmi, Nagamani.K, S.Sujatha 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 The DNN-based water leakage detection system project has introduced an innovative approach to identify leakages in water distribution networks using Deep Neural Networks (DNNs). Traditional methods for detecting leaks often depend on manual inspection or costly sensor networks, which may not scalable or cost-effective. In contrast, this project applies the power of DNNs to automatically scrutinize audio data captured by microphones and detect leaks in real-time. The system operates by training a DNN model on a dataset of audio recordings containing both normal pipeline sounds and leak-related abnormalities. The trained model is then put in place to incessantly monitor audio data from strategically placed microphones along the pipeline network. By analyzing the audio signals using the trained DNN, the system can accurately identify and localize potential leaks with high precision using DNNs for leak detection, the project presents several advantages, including improved accuracy, scalability, and cost-effectiveness. Further-more, the system’s capacity to work in real-time enables water utilities to proactively detect and address leaks before they amplify into larger issues, thus reducing water loss and minimizing environmental impact. In general, the DNN-based water leakage detection system project represents a noteworthy advancement in the field of leak detection, offering an innovative solution that merges machine learning with audio signal processing to ensure the integrity of water distribution networks.
Artificial Neural Networks-Based Machine learning for Analysis of Sub-surface Water quality B.Anuradha, Sheena. A. D, Hemamalini. J, S. Packialakshmi, Nagamani.K, T.UdayaBanu 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 Ground water is the most important source of water for the entire world. In India, almost 65% of the water needs for various purposes are met by ground water. Improper disposal of several waste, the ground water is being contaminated includes presence of heavy metals and toxic substance. There are other certain technologies adopted to predict the quality of water. The accuracy will not be high due to several factors like climate change, Temperature change, and pollution. Machine Learning technologies can be adapted to predict the quality of water. Different Algorithms are used to train the model and the accuracy of all algorithms is then compared to check which algorithm has the highest accuracy to predict the quality of the water.
Land Use Classification with different Machine Learning technique with Landsat MSS Image Nagamanai Katukotta, Stephen Jayaseelan, Atchuthan Purushothaman, Baskaran Anuradhha, Shanmugam Packialakshmi, T. UdayaBanu 2025 International Conference on Data Science Agents and Artificial Intelligence Icdsaai 2025, 2025 Land use/land cover changes had accelerated near the turn of the 20th century owing to rapid and unregulated population growth, economic activity, and industrialization, especially in emerging nations. A quantitative assessment evidently is imperative for grasping and controlling land transformation processes. In this study, an assessment is made of the performance of four commonly used machine-learning algorithms-Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM), for LULC mapping. Kappa statistics used to assess the accuracy of the study helps to see that all classifiers present accurate results, though some variation is present. The highest accuracy (0.89 Kappa) is attained through the Random Forest algorithm. These results highlight SVM and RF out of all the algorithms used for LULC mapping as the most effective classifiers. However, more tests with the RF algorithm should be conducted in various regions and climate settings to confirm its robustness and potential applicability.
Understanding flash flooding in the Himalayan Region: a case study Katukotta Nagamani, Anoop Kumar Mishra, Mohammad Suhail Meer, Jayanta Das Scientific Reports, 2024 The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.
5G and Cognitive Radio K. Nagamani, R. Bhagya Digital Convergence in Antenna Designs Applications for Real Time Solutions, 2024 In the present scenario, due to the advancement in wireless and mobile technology, there has been a significant increase in the number of wireless devices and also an increase in smart technology. Mobile and wireless communication will increasingly become the primary media for humans and machines to access information to provide services. This will lead to socioeconomic changes including improvements in productivity, sustainability, entertainment and well-being. There is significant growth in the Internet of Things (IoT) due to an enormous increase in the connected devices in every sector. There is huge demand for the spectrum every hour as more and more devices are connected. The spectrum is limited; it has to be used in a smart way as there is no free spectrum available to support the huge demand of the connected wireless devices and to support the data traffic. The spectrum which is allocated to the licensed users is not optimally utilized; sometimes it is overused and sometimes it is underutilized. Fifth-generation network (5G) supports significantly faster mobile broadband speeds and heavier data usage than previous generations and also enables the full potential of the Internet of Things. Due to an extensive advancement in the 5G technology, one of the important technologies used is the Cognitive radio (CR) system, which is expected to be one of the technical solutions of innovation and development of future wireless systems. The CR system in the 5G network is finding an emerging and potential application by employing CR capabilities used to overcome the shortfall of the spectrum and optimally make use of the available spectrum. The CR dynamically senses the free spectrum available or unutilized and allots to the unlicensed users without affecting the licensed users. This provides a more efficient way of using the limited radio resources.
Multifactor Authentication Using Blockchain in 6G Aishwarya R, Anusha Khot, Sumana Sreenivas, Nagamani K 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2024, 2024 With the rise of 6G technology, cyberattacks have become a significant concern, necessitating robust data security measures. Multi-factor authentication (MFA) systems, integrating advanced biometric methods, offer a promising solution to enhance security and protect sensitive information. This work explores the integration of iris and voice recognition with blockchain technology to enhance security measures for 6G networks. The increasing complexity of cyberattacks, privacy concerns from massive data collection, and the vulnerabilities associated with edge computing and quantum computing highlight the necessity for robust security solutions. Gaps in current 6G network security research, including limited studies on integrating multiple biometric modalities within Multi-Factor Authentication (MFA) systems and the lack of practical integration of AI/ML, Distributed Ledger Technology, and quantum computing have been identified. Addressing these gaps can lead to scalable and effective security solutions for 6G networks. The combination of unique iris and voice patterns with the tamper-proof and decentralized nature of blockchain ensures a robust authentication process. This framework was implemented and its efficacy and feasibility were demonstrated through simulated authentication procedures. The results show a significant improvement in security metrics over conventional single-factor authentication methods, with high accuracy rates for both voice and iris recognition. These outcomes, along with the blockchain's tamper-proof features, provide a strong defense against unauthorized access.
Investigation on road conditions of Sholinganallur taluk, Chennai, using remote sensing and geographic information system International Journal of Recent Technology and Engineering, 2019
Evaluation of coastal aquaculture ponds using remote sensing and GIS Indian Journal of Geo Marine Sciences, 2019
Aquifer mapping using VES augmented by dar zarrouk method in thirukkazhukundram block, Tamil Nadu, India 9th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2018, 2018
Demarcation and validation of flood susceptibility hazard zone mapping by AHP for the Swarna River Basin, Southern Part of the West Coast India KT Nagamani, TN Bhagwat, BR Manjunatha, HK Ramaraju Discover Applied Sciences , 2026 2026
Classification, Quantification and Management of Marine Litter Along the Covelong Beach of Tamil Nadu R Nagalakshmi, A Joseph, V Aswath Balaji, V Saichand, K Nagamani, ... Remote Sensing, GIS and Modelling for Water Resource Management: Volume 2 … , 2025 2025
Evaluating Groundwater Pollution Risk Vulnerability and Mitigation Techniques in a Changing Climate K Nagamani, MS Meer, B Pradeep Kumar Groundwater Resource Management Planning Strategies: A Geospatial Approach … , 2025 2025 Citations: 1
Application of Satellite and Geospatial technology for flash flood mapping over Himalayan with reference to Dharamshala in Himachal Pradesh, India during July 2021. K Nagamani, MS Meer, AK Mishra, MR Sheriff, MA Najar Journal of Applied & Natural Science 16 (2) , 2024 2024 Citations: 8
Hydro-Morphometric Analysis for Flood Potential Assessment in Swarna Watershed, Karnataka, India-Implication on Coastal Water Conservation and Protection KT Nagamani, TN Bhagwat Water Conservation Science and Engineering 9 (2), 52 , 2024 2024
Understanding flash flooding in the Himalayan Region: a case study K Nagamani, AK Mishra, MS Meer, J Das Scientific Reports 14 (1), 7060 , 2024 2024 Citations: 89
Understanding flash flooding in the Himalayan Region: a case study. Sci Rep 14: 7060 K Nagamani, AK Mishra, MS Meer, J Das 2024 Citations: 6
Understanding flash flooding in the Himalayan Region: a case study. Sci Rep 14 (1): 7060 K Nagamani, AK Mishra, MS Meer, J Das 2024 Citations: 8
Mapping severe tropical cyclone tauktae across the Arabian Sea and Western Coast of India using remote sensing and machine learning during May 2021 K Nagamani, AK Mishra, MS Meer, B Anuradha 2023 International conference on data science, agents & artificial … , 2023 2023 Citations: 5
Identification of groundwater potential zones using machine learning algorithms and geospatial techniques K Nagamani, MS Meer, B Anuradhha, C Bhuvaneswari, S Packialakshmi 2023 International Conference on Data Science, Agents & Artificial … , 2023 2023 Citations: 1
Fluoride contamination of groundwater in a coastal region-a growing environmental pollution threat AA Sambhavi, K Nagamani, B Gowtham, S Packialakshmi, B Anuradha GLOBAL NEST JOURNAL 25 (9), 41-52 , 2023 2023 Citations: 2
Prediction of Algal Bloom and Its Effects on Aquaculture in Coastal Area Using Modis Dataset and Machine Learning Techniques K Srilatha, N Balraman, K Nagamani 2023 International Conference on Advances in Computing, Communication and … , 2023 2023 Citations: 5
Precipitation and Stream flow Trends for Swarna River Watershed, Karnataka KT Nagamani, SS Chethana, TN Bhagwat Climate Change Impact on Water Resources: Proceedings of 26th International … , 2023 2023
Impact of conducting hand hygiene audit in COVID-19 care locations of India—A large scale national multicentric study–HHAC study S Krishnamoorthi, K Priyadarshi, D Rajshekar, R Sundaramurthy, ... Indian Journal of Medical Microbiology 43, 39-48 , 2023 2023 Citations: 8
Hydrochemical investigation and water quality mapping in and around Pallikaranai marshland area in Chennai, India S Packialakshmi, K Nagamani, B Anuradha Impacts of Urbanization on Hydrological Systems in India, 25-42 , 2023 2023 Citations: 3
Comparison of hand hygiene compliance among healthcare workers in Intensive care units and wards of COVID-19: A large scale multicentric study in India S Dhandapani, D Rajshekar, K Priyadarshi, S Krishnamoorthi, ... American Journal of Infection Control 51 (3), 304-312 , 2023 2023 Citations: 23
Fluoride contamination of groundwater in a coastal region-a growing environmental pollution threat. A Amuthini Sambhavi, K Nagamani, B Gowtham, S Packialakshmi, ... 2023
Treatment of industrial wastewater using coconut shell based activated carbon S Packialakshmi, B Anuradha, K Nagamani, JS Devi, S Sujatha Materials Today: Proceedings 81, 1167-1171 , 2023 2023 Citations: 51
INTRODUCTION TO CLIMATE CHANGE AND CURRENT TRENDS IN INDIA R Nagalakshmi, K Nagamani, RS Devi Ecological Environment: A New Perspective, 242 , 2022 2022
Study of Indian coastal geomorphology K Nagamani, R Devi, PM Krishna Ecological Environment: A New Perspective 45 , 2022 2022 Citations: 3
MOST CITED SCHOLAR PUBLICATIONS
Understanding flash flooding in the Himalayan Region: a case study K Nagamani, AK Mishra, MS Meer, J Das Scientific Reports 14 (1), 7060 , 2024 2024 Citations: 89
Treatment of industrial wastewater using coconut shell based activated carbon S Packialakshmi, B Anuradha, K Nagamani, JS Devi, S Sujatha Materials Today: Proceedings 81, 1167-1171 , 2023 2023 Citations: 51
Land use/land cover in Pondicherry using remote sensing and GIS K Nagamani, S Ramachandran Proceedings of the third international conference on environment and health … , 2003 2003 Citations: 40
Aluminium formwork system using in highrise buildings construction R Thiyagarajan, V Panneerselvam, K Nagamani International Journal of Advanced Research in Engineering and Technology 8 … , 2017 2017 Citations: 32
Comparison of hand hygiene compliance among healthcare workers in Intensive care units and wards of COVID-19: A large scale multicentric study in India S Dhandapani, D Rajshekar, K Priyadarshi, S Krishnamoorthi, ... American Journal of Infection Control 51 (3), 304-312 , 2023 2023 Citations: 23
Remote sensing, GIS and crop simulation models: A review K Nagamani, VEN Mariappan, V Ethirajan Int J Curr Res Biosci Plant Biol 4 (8), 80-92 , 2017 2017 Citations: 14
Land use land cover changes on Asia’s largest freshwater lake and their impact on society and environment MS Meer, AK Mishra, K Nagamani Arabian Journal of Geosciences 15 (9), 830 , 2022 2022 Citations: 9
Environmental impact assessment of Thamirabarani River Basin, Tamil Nadu using remote sensing and GIS techniques P Mohana, K Nagamani, S Muthusamy, PM Velmurugan Indian J. Sci. Technol 11 (22), 17 , 2018 2018 Citations: 9
Application of Satellite and Geospatial technology for flash flood mapping over Himalayan with reference to Dharamshala in Himachal Pradesh, India during July 2021. K Nagamani, MS Meer, AK Mishra, MR Sheriff, MA Najar Journal of Applied & Natural Science 16 (2) , 2024 2024 Citations: 8
Understanding flash flooding in the Himalayan Region: a case study. Sci Rep 14 (1): 7060 K Nagamani, AK Mishra, MS Meer, J Das 2024 Citations: 8
Impact of conducting hand hygiene audit in COVID-19 care locations of India—A large scale national multicentric study–HHAC study S Krishnamoorthi, K Priyadarshi, D Rajshekar, R Sundaramurthy, ... Indian Journal of Medical Microbiology 43, 39-48 , 2023 2023 Citations: 8
Evaluation of coastal aquaculture ponds using remote sensing and GIS K Nagamani, Y Suresh Indian Journal of Geo-Marine Sciences 48 (8), 1205-1209 , 2019 2019 Citations: 8
Understanding flash flooding in the Himalayan Region: a case study. Sci Rep 14: 7060 K Nagamani, AK Mishra, MS Meer, J Das 2024 Citations: 6
Study on Error Matrix Analysis of Classified Remote Sensed Data for Pondicherry Coast JS K Nagamani,K Jayakumar, Yasodharan Suresh Journal of Advanced Research in Geo Sciences & Remote Sensing 2 (3&4), 148 - 154 , 2015 2015 Citations: 6
Mapping severe tropical cyclone tauktae across the Arabian Sea and Western Coast of India using remote sensing and machine learning during May 2021 K Nagamani, AK Mishra, MS Meer, B Anuradha 2023 International conference on data science, agents & artificial … , 2023 2023 Citations: 5
Prediction of Algal Bloom and Its Effects on Aquaculture in Coastal Area Using Modis Dataset and Machine Learning Techniques K Srilatha, N Balraman, K Nagamani 2023 International Conference on Advances in Computing, Communication and … , 2023 2023 Citations: 5
Hydrochemical investigation and water quality mapping in and around Pallikaranai marshland area in Chennai, India S Packialakshmi, K Nagamani, B Anuradha Impacts of Urbanization on Hydrological Systems in India, 25-42 , 2023 2023 Citations: 3
Study of Indian coastal geomorphology K Nagamani, R Devi, PM Krishna Ecological Environment: A New Perspective 45 , 2022 2022 Citations: 3
GIS-Based Surface Runoff Modeling Using Empirical Technique For A River Basin In South India BPD Batvari, K Nagamani Nature Environment and Pollution Technology 20 (5), 2117-2123 , 2021 2021 Citations: 3
Branching based underwater clustering protocol. RM Gomathi, JML Manickam, K Nagamani Indian Journal of Science and Technology 9 (30) , 2017 2017 Citations: 3
