{"title":"Integrating river discharge and Sentinel-2 satellite imagery for enhanced turbidity mapping in arid region rivers: A machine learning approach","authors":"Milad Ahmadi , Ashkan Noori , Seyed Hossein Mohajeri , Mohammad Reza Nikoo","doi":"10.1016/j.pce.2025.103869","DOIUrl":null,"url":null,"abstract":"<div><div>This study pioneers a novel mapping application, addressing the state of water quality assessment along Iran's Karun River in order to advance this field. The current work utilizes a state of the art machine learning method that seamlessly combines the inversion of multi-temporal satellite imagery with river discharge data. Focusing on the crucial water resources of arid regions, four machine learning models were developed and compared: These are K-Nearest Neighbors, Random Forest, Gradient Boosting and Multi-Layer Perceptron. The research introduces two key models: (I) R<sub>rs</sub>(Tur), which refers to remote sensing reflectance based retrieved turbidity maps, and the more advanced, (II) R<sub>rs</sub>&Q(Tur), which denote incorporating both remote sensing and river discharge data. The results show that the R<sub>rs</sub> & Q(Tur) model significantly outperforms its counterpart in Root Mean Square Error (≃15% Reduction) and Bias Errors (≃13%). By integrating hydrological and remote sensing data, this study demonstrates how to help environmental monitoring, especially in assessing water quality in arid regions through machine learning approaches.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"138 ","pages":"Article 103869"},"PeriodicalIF":3.0000,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics and Chemistry of the Earth","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474706525000191","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This study pioneers a novel mapping application, addressing the state of water quality assessment along Iran's Karun River in order to advance this field. The current work utilizes a state of the art machine learning method that seamlessly combines the inversion of multi-temporal satellite imagery with river discharge data. Focusing on the crucial water resources of arid regions, four machine learning models were developed and compared: These are K-Nearest Neighbors, Random Forest, Gradient Boosting and Multi-Layer Perceptron. The research introduces two key models: (I) Rrs(Tur), which refers to remote sensing reflectance based retrieved turbidity maps, and the more advanced, (II) Rrs&Q(Tur), which denote incorporating both remote sensing and river discharge data. The results show that the Rrs & Q(Tur) model significantly outperforms its counterpart in Root Mean Square Error (≃15% Reduction) and Bias Errors (≃13%). By integrating hydrological and remote sensing data, this study demonstrates how to help environmental monitoring, especially in assessing water quality in arid regions through machine learning approaches.
期刊介绍:
Physics and Chemistry of the Earth is an international interdisciplinary journal for the rapid publication of collections of refereed communications in separate thematic issues, either stemming from scientific meetings, or, especially compiled for the occasion. There is no restriction on the length of articles published in the journal. Physics and Chemistry of the Earth incorporates the separate Parts A, B and C which existed until the end of 2001.
Please note: the Editors are unable to consider submissions that are not invited or linked to a thematic issue. Please do not submit unsolicited papers.
The journal covers the following subject areas:
-Solid Earth and Geodesy:
(geology, geochemistry, tectonophysics, seismology, volcanology, palaeomagnetism and rock magnetism, electromagnetism and potential fields, marine and environmental geosciences as well as geodesy).
-Hydrology, Oceans and Atmosphere:
(hydrology and water resources research, engineering and management, oceanography and oceanic chemistry, shelf, sea, lake and river sciences, meteorology and atmospheric sciences incl. chemistry as well as climatology and glaciology).
-Solar-Terrestrial and Planetary Science:
(solar, heliospheric and solar-planetary sciences, geology, geophysics and atmospheric sciences of planets, satellites and small bodies as well as cosmochemistry and exobiology).