Conference Papers
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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.