Water quality monitoring of Al-Habbaniyah Lake using remote sensing and in situ measurements

2015 ◽  
Vol 187 (6) ◽  
Author(s):  
Ahmed A. H. AL-Fahdawi ◽  
Adel M. Rabee ◽  
Shaheen M. Al-Hirmizy
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
In Situ Measurements ◽  
Quality Monitoring

scholarly journals Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

2019 ◽  
Vol 11 (14) ◽  
pp. 1674 ◽  
Author(s):  
Fangling Pu ◽  
Chujiang Ding ◽  
Zeyi Chao ◽  
Yue Yu ◽  
Xin Xu
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Inland Lakes ◽  
Quality Classification ◽  
Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

scholarly journals Fundamentals of in Situ Digital Camera Methodology for Water Quality Monitoring of Coast and Ocean

Sensors ◽  
2009 ◽  
Vol 9 (7) ◽  
pp. 5825-5843 ◽  
Author(s):  
Lonneke Goddijn-Murphy ◽  
Damien Dailloux ◽  
Martin White ◽  
Dave Bowers
Keyword(s):  
Water Quality ◽  
Digital Camera ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Field Spectroscopy as a Tool for Enhancing Water Quality Monitoring in the ACE Basin, SC

2017 ◽  
pp. 41-55 ◽  
Author(s):  
Caitlyn C. Mayer ◽  
Khalid A. Ali
Keyword(s):  
Water Quality ◽  
South Carolina ◽  
Chlorophyll A ◽  
Algal Species ◽  
Water Quality Monitoring ◽  
Water Systems ◽  
Quality Monitoring ◽  
Least Squares Regression ◽  
Monitoring Methods

The Ashepoo, Combahee, Edisto (ACE) Basin in South Carolina is one of the largest undeveloped estuaries in the Southeastern United States. This system is monitored and protected by several government agencies to ensure its health and preservation. However, as populations in surrounding cities rapidly expand and land is urbanized, the surrounding water systems may decline from an influx of contaminants, leading to hypoxia, fish kills, and eutrophication. Conventional in situ water quality monitoring methods are timely and costly. Satellite remote sensing methods are used globally to monitor water systems and can produce an instantaneous synopsis of color-producing agents (CPAs), including chlorophyll-a, suspended matter (TSM), and colored-dissolved organic matter by applying bio-optical models. In this study, field, laboratory, and historical land use land cover (LULC) data were collected during the summers of 2002, 2011, 2015, and 2016. The results indicated higher levels of chlorophyll, ranging from 2.94 to 12.19 μg/L, and TSM values were from 60.4 to 155.2 mg/L between field seasons, with values increasing with time. A model was developed using multivariate, partial least squares regression (PLSR) to identify wavelengths that are more sensitive to chlorophyll-a (R2 = 0.49; RMSE = 1.8 μg/L) and TSM (R2 = 0.40; RMSE = 12.9 mg/L). The imbrication of absorption and reflectance features characterizing sediments and algal species in ACE Basin waters make it difficult for remote sensors to distinguish variations among in situ concentrations. The results from this study provide a strong foundation for the future of water quality monitoring and for the protection of biodiversity in the ACE basin.

scholarly journals Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

2020 ◽  
Vol 12 (10) ◽  
pp. 1586
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
Rodrigo Sepúlveda ◽  
Sergio I. Martinez-Martinez ◽  
Markus Disse
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Temporal Variations ◽  
Quality Parameters ◽  
Parameters Estimation ◽  
Learning Approaches ◽  
Effects Of Drought

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

Reservoir water quality monitoring using remote sensing with seasonal models: case study of five central-Utah reservoirs

2015 ◽  
Vol 31 (3) ◽  
pp. 225-240 ◽  
Author(s):  
Carly Hyatt Hansen ◽  
Gustavious P. Williams ◽  
Zola Adjei ◽  
Analise Barlow ◽  
E. James Nelson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Reservoir Water
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