Suitability Assessment of Cage Fish Farming Location in Reservoirs through Neural Networks-Based Remote Sensing Analysis

作     者：Sedighkia, Mahdi Datta, Bithin 

作者机构：Australian Natl Univ ICEDS Canberra 2601 Australia Australian Natl Univ MSI Canberra 2601 Australia James Cook Univ Coll Sci Townsville 4811 Australia 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2024年第16卷第2期

页      面：236页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

主　　题：remote sensing water quality artificial neural network neuro fuzzy inference system evolutionary algorithms 

摘      要：The present study evaluates the application of different artificial intelligence methods associated with remote sensing data processing for assessing water quality parameters, with a focus on fish cage farming in the reservoirs. Three AI methods were utilized including 1-optimal artificial neural network (ONN), 2-adaptive neuro fuzzy inference system in which a hybrid algorithm was used for the training process (ANFIS) and 3-coupled evolutionary algorithm-adaptive neuro fuzzy inference system in which particle swarm optimization was utilized in the training process (EA-ANFIS). Three critical water quality parameters for cage fish farming were selected consisting of water temperature, dissolved oxygen (DO) and total dissolved solids (TDS). Moreover, two measurement indices, the Nash-Sutcliffe model efficiency coefficient (NSE) and root mean square error (RMSE), were utilized to assess the predictive skills of the data driven models. Based on the results in the case study, EA-ANFIS is the best method to simulate water temperature and DO in the reservoir by the remote sensing technique. Furthermore, the ANFIS-based model is the best method to simulate TDS. According to the results in the case study, utilizing the spectral images might not be reliable to simulate DO concentration in the reservoirs. However, the images are robust to simulate water temperature as well as TDS concentration.