Mantena Sireesha

Scopus Publications

Advances in transfer learning for smart wastewater treatment plants: Learning frameworks and emerging pathways
Sireesha Mantena, Ameer Khan Patan, Purushottama Rao Dasari, Mohit Kushwaha, E.S.S. Tejaswini, Abdul Gaffar Sheik
Journal of Environmental Management, 2026
Wastewater treatment plants (WWTPs) must operate efficiently under varying influent conditions and rigorous regulations due to growing populations and industrial expansion. For complex processes such as nutrient removal, effluent quality, activated sludge behaviour, and aeration control, data collection is laborious, expensive, and expert-dependent, which slows machine learning and deep learning for water quality parameter prediction, fault detection, and process optimization. Transfer learning (TL) provides an efficient approach by allowing pre-trained models, developed on extensive generic or wastewater datasets, to be tailored for WWTP applications. TL enhances wastewater processes by transferring learned features that identify hydraulic patterns, pollutant indicators, reactor state transitions, and sludge characteristics, thus improving prediction accuracy in data-scarce environments. Recent studies indicate that TL improves the estimation of nutrient concentrations, sensor calibration, anomaly detection in aeration and settling units, and the early identification of sensor fouling and equipment faults. A comprehensive review focused on TL in WWTP is currently lacking, despite these benefits. Addressing this gap is crucial for informing the development and implementation of TL-based intelligent WWTP strategies. This review summarizes the framework of TL applications in WWTPs, examining previously explored pre-trained models, data sources, sensing and control modalities, computing platforms, and TL strategies. The primary challenges identified include data heterogeneity, the scarcity of benchmark datasets, model generalization, and dynamic operational conditions. Additionally, potential research directions for the integration of TL into next-generation WWTP are discussed.
Understanding the agriculture sectors of greenhouse gas emissions prediction in the global scenario: Insights from explainable artificial intelligence (XAI)
Mantena Sireesha, Abdul Gaffar Sheik
Atmospheric Pollution Research, 2026
Explainable machine learning framework for thermal performance modelling of non-Newtonian nanofluid in shell-and-helical coil heat exchanger
Abdul Gaffar Sheik, Sireesha Mantena, A. Venu Vinod, B. Anil Kumar Naik
Chemical Papers, 2026
The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential
Abdul Gaffar Sheik, Mantena Sireesha, Arvind Kumar, Purushottama Rao Dasari, Reeza Patnaik, Sourav Kumar Bagchi, Faiz Ahmad Ansari, Faizal Bux
Marine Pollution Bulletin, 2025
Recent advancements in data analytics, predictive modeling, and optimization have highlighted the potential of integrating algal blooms (ABs) with Industry 4.0 technologies. Among these innovations, digital twins (DT) have gained prominence, driven by the rapid development of artificial intelligence (AI) and machine learning (ML) technologies, particularly those associated with the Internet of Things (IoT). AI is pivotal in enabling IoT and DT by enhancing decision-making, automating processes, and delivering actionable insights. The intersection of DT and AI in the context of ABs presents a promising new area for research exploration. Digital twins, which serve as virtual replicas of physical entities, systems, or processes, offer significant potential when combined with AI technologies, paving the way for novel research avenues in algal management (AM). This literature review examines digital twins' challenges and applications within AM. It also comprehensively analyzes the current state of IoT-based applications developed using AI and DT. The review further explores the tools for implementing DT systems and surveys existing AI techniques incorporating DTs. Additionally, it discusses the opportunities and challenges associated with creating various IoT-based applications by integrating AI and DT. The review concludes by identifying unexplored research avenues in this emerging field, underscoring the potential for future advancements in Artificial Intelligence of Things (AIoT) within AM. • Algal blooms (ABs) transforming towards ABs-4.0 via data analytics with prediction and managing. • Deployment of ABs infrastructure with the aid of Industry 4.0 enabling technologies. • Digital twins, artificial intelligence, and the Internet of Things can serve as facilitating technologies. • The review outlined future directions for research and technology innovation in ABs.
Prediction of salinity intrusion in the east Upputeru estuary of India using hybrid metaheuristic algorithms
Sireesha Mantena, Vazeer Mahammood, Kunjam Nageswara Rao
Modeling Earth Systems and Environment, 2024
Feasibility Study on Application of Soft Computing Algorithms for Salinity Intrusion Mapping
Sireesha Mantena, Vazeer Mahammood, Kunjam Nageswara Rao
Lecture Notes in Civil Engineering, 2024
Assessment of Soil Salinity in the East Upputeru Catchment of Andhra Pradesh Using Geospatial Techniques
Sireesha Mantena, Vazeer Mahammood, Kunjam Nageswara Rao
Lecture Notes in Civil Engineering, 2024
Dynamics of the Aquacultural Intensification in the Godavari-Krishna Inter Delta Region in India and Its Impact on Ecological Balance
T. V. Nagaraju, T. Rambabu, Sireesha Mantena, B. M. Sunil
Lecture Notes in Civil Engineering, 2024
A Review on Application of Soft Computing Techniques in Geotechnical Engineering
T. V. Nagaraju, Mantena Sireesha, B. M. Sunil, Shaik Subhan Alisha
Lecture Notes in Civil Engineering, 2024
Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights
T. Vamsi Nagaraju, G. Sri Bala, Sridevi Bonthu, Sireesha Mantena
Science of the Total Environment, 2024
Prediction of soil salinity in the Upputeru river estuary catchment, India, using machine learning techniques
Sireesha Mantena, Vazeer Mahammood, Kunjam Nageswara Rao
Environmental Monitoring and Assessment, 2023
Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches
T. Vamsi Nagaraju, Sireesha Mantena, Marc Azab, Shaik Subhan Alisha, Chady El Hachem, Musa Adamu, Pilla Sita Rama Murthy
Results in Engineering, 2023
Geopolymer-stabilized soils: influencing factors, strength development mechanism and sustainability
T. Vamsi Nagaraju, Sireesha Mantena, R. Gobinath, Sridevi Bonthu, Shaik Subhan Alisha
Journal of Taibah University for Science, 2023
Prediction of Strength and Stiffness Behavior of Glass Powder Stabilized Expansive Clay Using ANN Principles
Shaik Subhan Alisha, T. V. Nagaraju, Kennedy C. Onyelowe, Venkateswarulu Dumpa, Mantena Sireesha
Lecture Notes in Civil Engineering, 2023
Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique
T. Vamsi Nagaraju, Alireza Bahrami, Ch. Durga Prasad, Sireesha Mantena, Monalisa Biswal, Md. Rashadul Islam
Buildings, 2023

RECENT SCHOLAR PUBLICATIONS

Explainable machine learning framework for thermal performance modelling of non-Newtonian nanofluid in shell-and-helical coil heat exchanger
AG Sheik, S Mantena, AV Vinod, B Naik
Chemical Papers, 1-18 , 2026
2026
Advances in transfer learning for smart wastewater treatment plants: Learning frameworks and emerging pathways
S Mantena, AK Patan, PR Dasari, M Kushwaha, ESS Tejaswini, AG Sheik
Journal of Environmental Management 402, 129078 , 2026
2026
Smart machines working alongside people with added resilience and sustainability goals in biological wastewater treatment plants
M Sireesha, MSPK Raju, SM Rafee
Digitalization of Biological Wastewater Treatment Plants, 39-64 , 2026
2026
Industry 4.0 or Industry 5.0? Overview of the biological wastewater treatment sector 4.0 and an insight into the potential of Industry 5.0
AG Sheik, M Sireesha, S Mishra, A Pandey, S Rodriguez-Couto
Digitalization of Biological Wastewater Treatment Plants, 1-22 , 2026
2026
Peer-Review Statements
M Sireesha
Conference on Social and Sustainable Innovation in Technology & Engineering … , 2025
2025
AI-Driven Prediction of Diwali Noise Pollution using Deep and Reinforcement Learning
GM Sree, M Sireesha, MS Pavan, K Raju, R Bonguluru, AG Sheik, ...
Conference on Social and Sustainable Innovation in Technology & Engineering … , 2025
2025
Human-Centric Validation of Reinforcement Learning–Based Control in Fluid Mechatronics: An Experimental Case Study
PS Chandra, GM Sree, M Sireesha, PR Dasari
Conference on Social and Sustainable Innovation in Technology & Engineering … , 2025
2025
Understanding the Agriculture Sectors of Greenhouse Gas Emissions Prediction in the Global Scenario: Insights from Explainable Artificial Intelligence (XAI)
M Sireesha, AG Sheik
Atmospheric Pollution Research, 102792 , 2025
2025
Citations: 2
The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential
FB Abdul Gaffar Sheik , Mantena Sireesha , Arvind Kumar , Purushottama Rao ...
Marine Pollution Bulletin 212 (117493) , 2024
2024
Citations: 6
Using Geospatial Techniques
S Mantena, V Mahammood, KN Rao
Recent Advances in Civil Engineering for Sustainable Communities: Select … , 2024
2024
Prediction of salinity intrusion in the east Upputeru estuary of India using hybrid metaheuristic algorithms
S Mantena, V Mahammood, KN Rao
Modeling Earth Systems and Environment 10 (1), 833-843 , 2024
2024
Citations: 3
Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights
TV Nagaraju, GS Bala, S Bonthu, S Mantena
Science of the Total Environment 906, 167386 , 2024
2024
Citations: 57
Geopolymer-stabilized soils: influencing factors, strength development mechanism and sustainability
TV Nagaraju, S Mantena, R Gobinath, S Bonthu, S Subhan Alisha
Journal of Taibah University for Science 17 (1), 2248651 , 2023
2023
Citations: 31
Dynamics of the Aquacultural
TV Nagaraju, T Rambabu, S Mantena, BM Sunil
Recent Developments in Water Resources and Transportation Engineering … , 2023
2023
Prediction of soil salinity in the Upputeru river estuary catchment, India, using machine learning techniques
S Mantena, V Mahammood, KN Rao
Environmental Monitoring and Assessment 195 (8), 1006 , 2023
2023
Citations: 32
Feasibility Study on Application of Soft Computing Algorithms for Salinity Intrusion Mapping
S Mantena, V Mahammood, KN Rao
International Conference on Interdisciplinary Approaches in Civil … , 2023
2023
Assessment of Soil Salinity in the East Upputeru Catchment of Andhra Pradesh Using Geospatial Techniques
S Mantena, V Mahammood, K Nageswara Rao
International Conference on Interdisciplinary Approaches in Civil … , 2023
2023
A review on application of soft computing techniques in geotechnical engineering
TV Nagaraju, M Sireesha, BM Sunil, SS Alisha
International conference on advances in civil and ecological engineering … , 2023
2023
Citations: 13
Expansive Clay Using ANN Principles
SS Alisha, TV Nagaraju, KC Onyelowe, V Dumpa, M Sireesha
Recent Developments in Geotechnics and Structural Engineering: Select … , 2023
2023
Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches
TV Nagaraju, S Mantena, M Azab, SS Alisha, C El Hachem, M Adamu, ...
Results in Engineering 17, 100973 , 2023
2023
Citations: 65

MOST CITED SCHOLAR PUBLICATIONS

Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches
TV Nagaraju, S Mantena, M Azab, SS Alisha, C El Hachem, M Adamu, ...
Results in Engineering 17, 100973 , 2023
2023.0
Citations: 65
Modelling biochemical oxygen demand in a large inland aquaculture zone of India: Implications and insights
TV Nagaraju, GS Bala, S Bonthu, S Mantena
Science of the Total Environment 906, 167386 , 2024
2024.0
Citations: 57
Predicting California bearing ratio of lateritic soils using hybrid machine learning technique
TV Nagaraju, A Bahrami, CD Prasad, S Mantena, M Biswal, MR Islam
Buildings 13 (1), 255 , 2023
2023.0
Citations: 35
Prediction of soil salinity in the Upputeru river estuary catchment, India, using machine learning techniques
S Mantena, V Mahammood, KN Rao
Environmental Monitoring and Assessment 195 (8), 1006 , 2023
2023.0
Citations: 32
Geopolymer-stabilized soils: influencing factors, strength development mechanism and sustainability
TV Nagaraju, S Mantena, R Gobinath, S Bonthu, S Subhan Alisha
Journal of Taibah University for Science 17 (1), 2248651 , 2023
2023.0
Citations: 31
A review on application of soft computing techniques in geotechnical engineering
TV Nagaraju, M Sireesha, BM Sunil, SS Alisha
International conference on advances in civil and ecological engineering … , 2023
2023.0
Citations: 13
Prediction of strength and stiffness behavior of glass powder stabilized expansive clay using ANN principles
SS Alisha, TV Nagaraju, KC Onyelowe, V Dumpa, M Sireesha
International conference on trends and recent advances in civil engineering … , 2022
2022.0
Citations: 12
Strength and stiffness prediction models of expansive clays blended with sawdust ash
SS Alisha, TV Nagaraju, PSR Murty, VVS Sarma, M Sireesha
IOP conference series: materials science and engineering 1273 (1), 012018 , 2023
2023.0
Citations: 9
The role of industry 4.0 enabling technologies for predicting, and managing of algal blooms: Bridging gaps and unlocking potential
FB Abdul Gaffar Sheik , Mantena Sireesha , Arvind Kumar , Purushottama Rao ...
Marine Pollution Bulletin 212 (117493) , 2024
2024.0
Citations: 6
Predicting California bearing ratio of lateritic soils using hybrid machine learning technique. Buildings 13 (1)(Jan 2023)
TV Nagaraju, A Bahrami, CD Prasad, S Mantena, M Biswal, MR Islam
Citations: 6
Prediction of salinity intrusion in the east Upputeru estuary of India using hybrid metaheuristic algorithms
S Mantena, V Mahammood, KN Rao
Modeling Earth Systems and Environment 10 (1), 833-843 , 2024
2024.0
Citations: 3
Understanding the Agriculture Sectors of Greenhouse Gas Emissions Prediction in the Global Scenario: Insights from Explainable Artificial Intelligence (XAI)
M Sireesha, AG Sheik
Atmospheric Pollution Research, 102792 , 2025
2025.0
Citations: 2
Development and Validation of UV Spectrophotometric Method for the Estimation of Ticagrelor (Oral Antiplatelet (OAP) in Pharmaceutical Dosage Form
P Ravisankar, M Sireesha, PS Babu, CP Vyshnavi, KD Raju
Int. J. Pharm. Sci. Rev. Res 62, 135-140 , 2020
2020.0
Citations: 2
Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique. Buildings 2023, 13, 255
TV Nagaraju, A Bahrami, CD Prasad, S Mantena, M Biswal, MR Islam
2023.0
Citations: 1
Dynamics of the Aquacultural Intensification in the Godavari-Krishna Inter Delta Region in India and Its Impact on Ecological Balance
TV Nagaraju, T Rambabu, S Mantena, BM Sunil
International Conference on Trends and Recent Advances in Civil Engineering … , 2022
2022.0
Citations: 1
Explainable machine learning framework for thermal performance modelling of non-Newtonian nanofluid in shell-and-helical coil heat exchanger
AG Sheik, S Mantena, AV Vinod, B Naik
Chemical Papers, 1-18 , 2026
2026.0
Advances in transfer learning for smart wastewater treatment plants: Learning frameworks and emerging pathways
S Mantena, AK Patan, PR Dasari, M Kushwaha, ESS Tejaswini, AG Sheik
Journal of Environmental Management 402, 129078 , 2026
2026.0
Smart machines working alongside people with added resilience and sustainability goals in biological wastewater treatment plants
M Sireesha, MSPK Raju, SM Rafee
Digitalization of Biological Wastewater Treatment Plants, 39-64 , 2026
2026.0
Industry 4.0 or Industry 5.0? Overview of the biological wastewater treatment sector 4.0 and an insight into the potential of Industry 5.0
AG Sheik, M Sireesha, S Mishra, A Pandey, S Rodriguez-Couto
Digitalization of Biological Wastewater Treatment Plants, 1-22 , 2026
2026.0
Peer-Review Statements
M Sireesha
Conference on Social and Sustainable Innovation in Technology & Engineering … , 2025
2025.0

Mantena Sireesha

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS