Department of Civil Engineering, SRKR Engineering College, Bhimavaram, India.
Centre for Clean and Sustainable Environment, SRKR Engineering College, Bhimavaram, India.
Environ Sci Pollut Res Int. 2024 May;31(25):36275-36290. doi: 10.1007/s11356-023-30206-3. Epub 2023 Oct 13.
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 (R) 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.
在过去的五十年中，渔业和虾类的产量每十年翻一番，成为增长最快的食品产业。这种增长是由于在南亚许多地区，密集型养殖和将农业转化为水产养殖所致。此外，密集型水产养殖带来了积极的经济增长，但如果没有适当的监测，也会导致环境恶化。不幸的是，水产养殖的技术创新比农业和制造业要少。遥感和软计算的出现为利用和整合民用和环境学科的技术进步提供了各种机会。本文介绍了安得拉邦西部戈达瓦里三角洲地区的水产养殖情况，并提出了各种新的评估工具来监测水产养殖环境。对内陆水产养殖池塘的物理化学特性进行了实验研究，以评估水产养殖池塘的水质。此外，为了评估内陆水产养殖的强度，目前的工作集中在遥感和软计算方法的潜在应用上。克里金和反距离加权（IDW）的地理空间模型在估计集约化水产养殖地下水中的氨水平方面表现出更高的性能，其决定系数（R）值分别为 0.947 和 0.901。在研究中使用的五种软计算技术中的两种，即基于教学学习的优化（TLBO）和自适应粒子群优化（APSO），表现优于其他技术。此外，还发现基于遥感的评估工具和软计算预测模型都值得信赖、准确且易于使用。此外，这些方法可以帮助利益相关者和政策制定者实时评估内陆水产养殖用水。
