{"title":"基于多光谱卫星图像的水质预测机器学习方法","authors":"Vicky Anand , Bakimchandra Oinam , Silke Wieprecht","doi":"10.1016/j.ecoinf.2024.102868","DOIUrl":null,"url":null,"abstract":"<div><div>Water quality analysis is a vital component of the water resources management and has to be undertaken promptly to make sure environmental regulations are being followed and to eliminate any pollution that could harm the ecosystem. The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). The model performance was evaluated based on coefficient of determination (R<sup>2</sup>), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) statistical indices. The result of this study indicates that the SVM yielded the highest accuracy followed by the RF and MLR. The R<sup>2</sup>, MAE, MAPE and RMSE ranged between 0.78 and 0.99, 0.049–0.24, 0.01–10.9 % and 0.05–0.28 respectively for all the four SVM models across both the sensors. Based on the spatial trend Sentinel-2 was found to be slightly superior to the ResourceSat-2 (LISS-IV) for the estimation of water quality parameters owing to its superior spectral and radiometric resolution, nevertheless ResourceSat-2 (LISS-IV) has its own advantage in terms of high spatial resolution. The results of this study highlight the high potential of machine learning models in conjunction with multispectral satellite images to manage water quality.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning approach for water quality predictions based on multispectral satellite imageries\",\"authors\":\"Vicky Anand , Bakimchandra Oinam , Silke Wieprecht\",\"doi\":\"10.1016/j.ecoinf.2024.102868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Water quality analysis is a vital component of the water resources management and has to be undertaken promptly to make sure environmental regulations are being followed and to eliminate any pollution that could harm the ecosystem. The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). The model performance was evaluated based on coefficient of determination (R<sup>2</sup>), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) statistical indices. The result of this study indicates that the SVM yielded the highest accuracy followed by the RF and MLR. The R<sup>2</sup>, MAE, MAPE and RMSE ranged between 0.78 and 0.99, 0.049–0.24, 0.01–10.9 % and 0.05–0.28 respectively for all the four SVM models across both the sensors. Based on the spatial trend Sentinel-2 was found to be slightly superior to the ResourceSat-2 (LISS-IV) for the estimation of water quality parameters owing to its superior spectral and radiometric resolution, nevertheless ResourceSat-2 (LISS-IV) has its own advantage in terms of high spatial resolution. The results of this study highlight the high potential of machine learning models in conjunction with multispectral satellite images to manage water quality.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574954124004102\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954124004102","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Machine learning approach for water quality predictions based on multispectral satellite imageries
Water quality analysis is a vital component of the water resources management and has to be undertaken promptly to make sure environmental regulations are being followed and to eliminate any pollution that could harm the ecosystem. The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). The model performance was evaluated based on coefficient of determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) statistical indices. The result of this study indicates that the SVM yielded the highest accuracy followed by the RF and MLR. The R2, MAE, MAPE and RMSE ranged between 0.78 and 0.99, 0.049–0.24, 0.01–10.9 % and 0.05–0.28 respectively for all the four SVM models across both the sensors. Based on the spatial trend Sentinel-2 was found to be slightly superior to the ResourceSat-2 (LISS-IV) for the estimation of water quality parameters owing to its superior spectral and radiometric resolution, nevertheless ResourceSat-2 (LISS-IV) has its own advantage in terms of high spatial resolution. The results of this study highlight the high potential of machine learning models in conjunction with multispectral satellite images to manage water quality.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.