Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

Browse

Filters

2023 - 20242

Settings

Subject: search.filters.subject.Soft computing

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Understanding the Role of Biological Oxygen Demand in Aquaculture Waters in the Western Delta Region of Andhra Pradesh
    (Springer Science and Business Media Deutschland GmbH, 2023) Thotakura, T.V.; Sunil, B.M.; Chaudhary, B.
    The aquaculture industry with intensive farming activities has been gaining potential benefits to the nation’s economic growth and food security. However, due to intensive farming, harmful pollutants are emerging with a higher concentration of biological oxygen demand (BOD), chemical oxygen demand (COD), and ammonia nitrates are more concerns. This study presents the role and assessment of BOD for the aquaculture ponds in the western delta region of Andhra Pradesh. The collected water samples at various locations in the study area have been tested for physicochemical characteristics and the test data used for BOD prediction. For evaluating the sensitivity of the prediction model, particle swarm optimization (PSO) with variations of inertia weight and damping factors is used to obtain the best global solution. As a result, prediction models developed for assessing BOD using PSO show convergent predictions. So, based on the prediction results, the implementation of prediction models of BOD using PSO could be helpful for sustainable aquaculture management in the western delta region of Andhra Pradesh. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    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.