Faculty Publications
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Item Impact of Aquaculture Solid Waste on Environment in the Delta Region of Andhra Pradesh: A Case Study(Springer Science and Business Media Deutschland GmbH, 2023) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.Aquaculture solid waste (ASW) from the aquaculture ponds is emerging waste which impacts on the environment due to intensive culture practices. In intensive aquaculture ponds, 45–65% of the dry weight of waste (shells, fins, and bones), surplus feed, chemicals, and minerals. This has led to a decline in the quality of the water used for aquaculture, environmental pollution, the occurrence of aquatic diseases, and even ecological imbalance, which has become a significant concern. This study presents the leachate characteristics and groundwater characteristics of the nearby dump sites. Field surveys were carried to know the source and disposal of ASW and to identify the lacunae of practice. Based on the leachate characteristics, it has been suggested that proper management of ASW is needed. This study also explores the Indian ASW and its impact on environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Assessment of Nitrate Fluxes in Intensive Aquaculture Region in Godavari Delta Using Spatial Interpolation Kriging(Springer Science and Business Media Deutschland GmbH, 2024) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.; Rambabu, T.In areas with a high concentration of intense aquaculture, nitrate pollution and nutrient enrichment are growing concerns. With predicted future climate changes, these problems are expected to intensify for aquifers and surface waters. The possibility exists to reduce some of these worries through land management and utilization modifications. However, there is much ambiguity surrounding how these alterations will relate. This article uses conventional kriging and empirical Bayesian kriging (EBK) to estimate nitrate levels in India’s intensive aquaculture zone, the Godavari delta. The stable, exponential, rational quadratic, and Gaussian models were used to fit experimental variograms using weighted least squares. The number of neighbors that generated the best cross-validation outcome has been further investigated for the model with the shortest residual sum of the squares. Kriging’s statistical approaches provided the best root mean square error (RMSE) values overall. No additional summary statistics shed any light on the regression method’s selection or settings. After thorough testing, we concluded that many parameters might be better detected using cross-validation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Prediction of Inland Aquaculture Ammonia Using Hybrid Intelligent Soft Computing(Springer Science and Business Media Deutschland GmbH, 2024) Thotakura, T.V.; Bala, G.; Durga Prasad, C.; Sunil, B.M.One of the crucial factors in assessing the pond's intensive inland aquaculture water quality condition is ammonia. The excessive ammonia content will likely worsen water quality and result in the mass mortality of cultured individuals. For aquaculture management, it is therefore vital to accurately identify the ammonia nitrogen level of cultured water. However, the accuracy of technology for monitoring the ammonia content of aquaculture water currently needs to be improved to satisfy the demands of intensive aquaculture. This paper presents the prediction of the ammonia concentration of aquaculture water in real time using a hybrid intelligent soft computing algorithm. Radial basis function neural networks (RBFNN) and a hybrid model combining RBFNN, and particle swarm optimization (PSO) are used in this technique. Root mean square error (RMSE) and correlation coefficient (R2) were two separate statistical metrics used to compare the two methodologies and assess how well the soft computing strategies performed. The ammonia prediction results showed that the PSO-RBFNN method outperformed the RBFNN. The PSO-RBFNN model offers a real-time ammonia prediction value in inland farming waters that is moderately and generally accurate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Multivariate Statistical Approach for Assessment of Surface Water Quality in the Intensive Inland Aquaculture Region in India(Springer Science and Business Media Deutschland GmbH, 2024) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.; Bala, G.Andhra Pradesh's western Godavari Delta region is India's primary inland aquaculture zone, with a well-developed canal network. The Venkaya-Vayyeru canal is the most important canal stream depending on aquaculture catchment. It also serves as a drinking water supply for villages, aquaculture, and irrigation. As a result, the quality of the canal's water is essential. To determine the probable location as a pollution source, this study considered eight physicochemical water quality characteristics at three distinctive places along the canal for principal component analysis (PCA). Most parameters show significant geographical variation, indicating anthropogenic influence. According to PCA findings, the principal pollution sources are aquaculture ponds, processing businesses, and urban activities. Aquaculture intensively may contaminate canal water with salinity, ammonia, and Ca2+. Aquaculture effluents, soluble salts, nutrients, and organic matter were found to be the essential parameters responsible for changes in water quality using PCA and factor analysis. The study demonstrates the usefulness of multivariate statistical methods in understanding a pattern of feature variability and devising management techniques to enhance canal water quality by identifying prevailing characteristics that cause the most degradation in the water quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Prediction of ammonia contaminants in the aquaculture ponds using soft computing coupled with wavelet analysis(Elsevier Ltd, 2023) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.; Durga Prasad, C.D.; Gobinath, G.Intensive aquaculture practices generate highly polluted organic effluents such as biological oxygen demand (BOD), alkalinity, total ammonia, nitrates, calcium, potassium, sodium, iron, and chlorides. In recent years, Inland aquaculture ponds in the western delta region of Andhra Pradesh have been intensively expanding and are more concerned about negative environmental impact. This paper presents the water quality analysis of aquaculture waters in 64 random locations in the western delta region of Andhra Pradesh. The average water quality index (WQI) was 126, with WQI values ranging from 21 to 456. Approximately 78% of the water samples were very poor and unsafe for drinking and domestic usage. The mean ammonia content in aquaculture water was 0.15 mg/L, and 78% of the samples were above the acceptable limit set by the World Health Organization (WHO) of 0.5 mg/L. The quantity of ammonia in the water ranged from 0.05 to 2.8 mg/L. The results show that ammonia levels exceed the permissible limits and are a significant concern in aquaculture waters due to toxicity. This paper also presents an intelligent soft computing approach to predicting ammonia levels in aquaculture ponds, using two novel approaches, such as the pelican optimization algorithm (POA) and POA coupled with discrete wavelet analysis (DWT-POA). The modified and enhanced POA with DWT can converge to higher performance when compared to standard POA, with an average percentage error of 1.964 and a coefficient of determination (R2) value of 0.822. Moreover, it was found that prediction models were reliable with good accuracy and simple to execute. Furthermore, these prediction models could help stakeholders and policymakers to make a real-time prediction of ammonia levels in intensive farming inland aquaculture ponds. © 2023 Elsevier LtdItem Novel assessment tools for inland aquaculture in the western Godavari delta region of Andhra Pradesh(Springer, 2024) Thotakura, T.V.; Malegole, S.B.; Chaudhary, B.; Gobinath, G.; Chitturi, P.; Durga Prasad, D.P.The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring. Unfortunately, technical innovation is less in aquaculture than agricultural and manufacturing industries. The advent of remote sensing and soft computing has expanded various opportunities for utilizing and integrating technological advances in civil and environmental disciplines. This paper presents the aquaculture scenario in the western Godavari delta region of Andhra Pradesh and proposes various novel assessment tools to monitor the aquaculture environment. An experimental investigation was carried out on the physicochemical characteristics of the inland aquaculture ponds to evaluate water quality in the aquaculture ponds. Furthermore, to assess the intensity of inland aquaculture, the current work concentrates on the potential application of remote sensing and soft computing approaches. Geospatial models of kriging and inverse distance weighing (IDW) show higher performance in estimating ammonia levels in the intensive aquaculture groundwaters with coefficient of determination (R2) values of 0.947 and 0.901, respectively. Teaching learning-based optimization (TLBO) and adaptive particle swarm optimization (APSO), two of the five soft computing techniques utilized in the study, perform better than the others. Additionally, it was found that remote sensing-based assessment tools and soft computing prediction models were both trustworthy, accurate, and easy to use. Furthermore, these methods could assist in the real-time evaluation of inland aquaculture waters by stakeholders and policymakers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.