Water Quality Monitoring of Water Resources Conservation Area in City of Shanghai Based on Remote Sensing

2006 ◽  
Author(s):  
Y. Qiu ◽  
H. Zhang ◽  
X. Tong ◽  
L. Chen ◽  
J. Zhao
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Resources ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Conservation Area ◽  
Water Resources Conservation ◽  
Resources Conservation

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 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.

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

Monitoring and predicting water quality for smart urban water infrastructure

2020 ◽  
Author(s):  
Elisa Coraggio ◽  
Dawei Han ◽  
Theo Tryfonas ◽  
Weiru Liu
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Real Time ◽  
Urban Areas ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Polluted Water ◽  
Clean Water ◽  
Urban Water ◽  
Resources Management

<p>Water resources management is a delicate, complex and challenging task. It involves monitoring quality, quantity, timing and distribution of water in order to meet the needs of the population’s usage demand. Nowadays these decisions have to be made in a continuously evolving landscape where quantity and quality of water resources change in time with uncertainty.</p><p>Throughout history, access to clean water has always been a huge desire from urban settlements. People built towns and villages close to water sources. In most cases, streams brought clean water in and washed away polluted water. Nowadays the largest strains on water quality typically occur within urban areas, with degradation coming from point and diffuse sources of pollutants and alteration of natural flow through built-up areas.</p><p>Municipalities are acting to reduce the impact of climate change on existing cities and meet the needs of the growing urban population. In many places around the world costal flood defences were built involving construction of barriers that lock the tide and keep the water coming from in-land rivers creating reservoirs close to the shore.</p><p>These man-made barriers stop the natural cleaning action of the tide on transitional waters. This causes severe water quality problems like eutrophication and high levels of bacteria. On the positive side, these water reservoirs are used as recreational water, drinking water, agricultural water. As many more people are moving to live in urban areas, its overall demand for clean water and discharge of polluted water is constantly growing. Hence monitoring and foreseeing water quality in these urban surface waters is fundamental in order to be able to meet the water demand in future scenarios.</p><p>Many cities have already successfully implemented smart water technologies in many types of the water infrastructures. Monitoring water quality has always been a challenging and costly task. It has been so far the most difficult water characteristic to monitor remotely in real time. Lack of high frequency and accurate data has always been one of the main challenges. Today, using information and communication technologies (ICT) is possible to set up a real time water quality monitoring system that will allow to deepen the understanding of water quality dynamics leading to a better management of urban water resources.</p><p>A case study will be presented where a real time water quality monitoring system for the surface water of Bristol Floating Harbour has been deployed in the UK and water quality data have been analysed using artificial intelligence algorithms in order to understand the link between ambient weather data (i.e., precipitation, temperature, solar radiation, wind, etc.) and surface water pollution. Preliminary results of a water quality prediction model will also be presented showing the capabilities of predicting water quality as a new tool in municipality’s decision-making processes and water resources management.</p>

scholarly journals Monitoring Water Quality of the Haihe River Based on Ground-Based Hyperspectral Remote Sensing

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 22
Author(s):  
Qi Cao ◽  
Gongliang Yu ◽  
Shengjie Sun ◽  
Yong Dou ◽  
Hua Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Real Time ◽  
Training Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Haihe River

The Haihe River is a typical sluice-controlled river in the north of China. The construction and operation of sluice dams change the flow and other hydrological factors of rivers, which have adverse effects on water, making it difficult to study the characteristics of water quality change and water environment control in northern rivers. In recent years, remote sensing has been widely used in water quality monitoring. However, due to the low signal-to-noise ratio (SNR) and the limitation of instrument resolution, satellite remote sensing is still a challenge to inland water quality monitoring. Ground-based hyperspectral remote sensing has a high temporal-spatial resolution and can be simply fixed in the water edge to achieve real-time continuous detection. A combination of hyperspectral remote sensing devices and BP neural networks is used in the current research to invert water quality parameters. The measured values and remote sensing reflectance of eight water quality parameters (chlorophyll-a (Chl-a), phycocyanin (PC), total suspended sediments (TSS), total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4-N), nitrate-nitrogen (NO3-N), and pH) were modeled and verified. The results show that the performance R2 of the training model is above 80%, and the performance R2 of the verification model is above 70%. In the training model, the highest fitting degree is TN (R2 = 1, RMSE = 0.0012 mg/L), and the lowest fitting degree is PC (R2 = 0.87, RMSE = 0.0011 mg/L). Therefore, the application of hyperspectral remote sensing technology to water quality detection in the Haihe River is a feasible method. The model built in the hyperspectral remote sensing equipment can help decision-makers to easily understand the real-time changes of water quality parameters.

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