scholarly journals Remote sensing inversion of lake water quality parameters based on ensemble modelling

2020 ◽  
Vol 143 ◽  
pp. 02007
Author(s):  
Li Xiaojuan ◽  
Huang Mutao ◽  
Li Jianbao
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Entropy Weight ◽  
Ensemble Modelling ◽  
Chl A ◽  
Inversion Model ◽  
Entropy Weight Method ◽  
Weight Method

In this paper, combined with water quality sampling data and Landsat8 satellite remote sensing image data, the inversion model of Chl-a and TN water quality parameter concentration was constructed based on machine learning algorithm. After the verification and evaluation of the inversion results of the test samples, Chl-a TN inversion model with high correlation between model test results and measured data was selected to participate in remote sensing inversion ensemble modelling of water quality parameters. Then, the ensemble remote sensing inversion model of water quality parameters was established based on entropy weight method and error analysis. By applying the idea of ensemble modelling to remote sensing inversion of water quality parameters, the advantages of different models can be integrated and the precision of water quality parameters inversion can be improved. Through the evaluation and comparative analysis of the model results, the entropy weight method can improve the inversion accuracy to some extent, but the improvement space is limited. In the verification of the two methods of ensemble modelling based on error analysis, compared with the optimal results of a single model, the determination coefficient (R2) of Chlorophyll a and TN concentration inversion results was increased from 0.9288 to 0.9313 and from 0.8339 to 0.8838, and the root mean square error was decreased from 14.2615 μ/L to 10.4194 μ/L and from1.1002mg/L to 0.8621mg/L. At the same time, with the increase of the number of models involved in the set modelling, the inversion accuracy is higher.

Integrated Fuzzy Evaluation of Raw Water Eutrophication Based on GIS

2014 ◽  
Vol 580-583 ◽  
pp. 2350-2353
Author(s):  
Jun Ping Liu ◽  
Jia Wei Shao ◽  
Fei Long Dong ◽  
Xiao Yan Ma
Keyword(s):  
Water Quality ◽  
Fuzzy Evaluation ◽  
Raw Water ◽  
Entropy Weight ◽  
Chl A ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Water Plants ◽  
Water Eutrophication ◽  
Quality Indexes

To do a research about raw water eutrophication of water plants in Hangzhou, samples of raw water in Jiuxi, Xiangfu, Nanxing, Qingtai Plant were collected and analyzed to get more than 10 water quality indexes. Turbidity, DO, COD, TN, NH3-N and CHL-a serving as example. supported by GIS, integrated fuzzy evaluations of raw water eutrophication in July and December were completed applying entropy weight method. The result shows that in terms of the nutrient degree, Xiangfu is the most qualified while the least one is Qingtai. Overall, raw water in December is of higher quality than July.

scholarly journals Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2844
Author(s):  
Yanhu He ◽  
Zhenjie Gong ◽  
Yanhui Zheng ◽  
Yuanbo Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Water Quality ◽  
Bp Neural Network ◽  
Quality Parameters ◽  
Remote Sensing Images ◽  
Linear Inversion ◽  
Reservoir Water ◽  
Chl A ◽  
Inversion Model

In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.

Adaptive ML-Models for analysis of TSS, Chlorophyll-a, in mixed water type scenarios in Ca Mau peninsula, Vietnam

2021 ◽  
Author(s):  
Long Vu Huu ◽  
Andreas Schenk ◽  
Stefan Hinz
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Surface Water ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Water Type ◽  
Water Types ◽  
Chl A ◽  
Sentinel 2

<p>The multispectral mission of Sentinel-2 enables reliable, affordable and continuous environmental monitoring systems in fields like agriculture, biodiversity, environmental hazards and surface water. Several studies have proven that main water quality parameters like total suspended solids (TSS) and chlorophyll (Chl-a) can be estimated from multispectral data using different methods and algorithms. However, independently of the specific approach, these algorithms are selected and optimized to work primarily for one of the main water types i.e. open water, coastal water or inland water. This is also shown by the fact that there is not a single universal algorithm, which can be applied to all water types with consistent and reliable performance at the same time.</p><p>Ca Mau peninsula is a spacious area located in the southern part of the Mekong Delta, with an area of around 1.6 million hectares. This area has high growth rates of agricultural and aquaculture production, hence diverse water demands and water use types. In this study we use Sentinel-2 remote sensing data to monitor surface water quality using adaptive ML models to account for the different surface water types which occur in this area. Through using remote sensing data, we can provide a synoptic and sufficient view in spatial aspects about water quality parameters in the Ca Mau peninsula. Adapting the ML model will address the bio-optical model for a mixed water scenario.</p><p>The study is based on Sentinel-2 satellite images acquired in 2019 and 2020, supplemented by field data, i.e. hyperspectral measurements using close range observations, in-situ measurements and water samples, with the aim to collect a comprehensive reference data set as biophysical parameters are closely connected with spectral parameters at close range as well as at high spectral resolution. Therefore, surface hyperspectral measurement has been used to simulate Sentinel 2 multispectral image data at the respective bands.</p><p>We automatically assign the water type classes to observed surface water by integrating GIS data and remote sensing as the pre-processing step. For each class, the ML models are trained based on the experimental measurements with the multispectral and the simulated multispectral images on the respective water types. We devote special attention to water type boundaries to provide a smooth transition of estimated parameters.</p><p>The outputs of this model are surface water quality distribution maps with turbidity, TSS, and Chl-a parameters for all areas in Ca Mau peninsula, independent of the actual water type. Through the acceptable accuracy of model testing, the consolidation model will contribute water quality parameters that are crucial and meaningful to the planning and use of water for domestic use and production, besides, it also supports the decision-making of sustainable water use.</p>

scholarly journals Deriving Water Quality Parameters Using Sentinel-2 Imagery: A Case Study in the Sado Estuary, Portugal

2021 ◽  
Vol 13 (5) ◽  
pp. 1043
Author(s):  
Giulia Sent ◽  
Beatriz Biguino ◽  
Luciane Favareto ◽  
Joana Cruz ◽  
Carolina Sá ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring ◽  
Chl A ◽  
Area Of Interest ◽  
Sado Estuary ◽  
Sentinel 2

Monitoring water quality parameters and their ecological effects in transitional waters is usually performed through in situ sampling programs. These are expensive and time-consuming, and often do not represent the total area of interest. Remote sensing techniques offer enormous advantages by providing cost-effective systematic observations of a large water system. This study evaluates the potential of water quality monitoring using Sentinel-2 observations for the period 2018–2020 for the Sado estuary (Portugal), through an algorithm intercomparison exercise and time-series analysis of different water quality parameters (i.e., colored dissolved organic matter (CDOM), chlorophyll-a (Chl-a), suspended particulate matter (SPM), and turbidity). Results suggest that Sentinel-2 is useful for monitoring these parameters in a highly dynamic system, however, with challenges in retrieving accurate data for some of the variables, such as Chl-a. Spatio-temporal variability results were consistent with historical data, presenting the highest values of CDOM, Chl-a, SPM and turbidity during Spring and Summer. This work is the first study providing annual and seasonal coverage with high spatial resolution (10 m) for the Sado estuary, being a key contribution for the definition of effective monitoring programs. Moreover, the potential of remote sensing methodologies for continuous water quality monitoring in transitional systems under the scope of the European Water Framework Directive is briefly discussed.

scholarly journals Entropy weight application for calculating groundwater quality index (EWQI) in groundwater quality zoning in Pleistocene aquifer in the Phu My town, Ba Ria – Vung Tau province

2020 ◽  
Vol 4 (1) ◽  
pp. 140-148
Author(s):  
Nguyen Hai Au ◽  
Tran Minh Bao ◽  
Pham Thi Tuyet Nhi ◽  
Tat Hong Minh Vy ◽  
Truong Tan Hien ◽  
...  
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Quality Index ◽  
Animal Husbandry ◽  
Quality Parameters ◽  
Entropy Weight ◽  
Groundwater Quality Index ◽  
Pleistocene Aquifer ◽  
Groundwater Samples ◽  
Entropy Weight Method

Groundwater in Phu My town is exploited essentially in Pleistocene aquifer and, used for many purposes like irrigation, domestic, production and animal husbandry. In this study, Groundwater Quality Index (EWQI) is calculated with Entropy weight method to determine the suitability of groundwater quality in study area. This method demonstrates the objectivity of each parameter calculated based on the degree of variability of each value and depends on the sample data source. The groundwater samples were collected from 17 wells in dry and wet seasons in 2017 with ten water quality parameters (pH, TDS, TH, Cl-, F-, NH4+-N, NO3--N, SO42-, Pb và Fe2+) were selected for analysising. The analysis results indicate groundwater quality is divided into 4 categories in this study area. In particular, over 70% of wells are "very good" water quality in both dry and wet seasons. Only 6% of wells are " water unsuitable for drinking purpose" of the total number of mornitoring wells in the study area.

scholarly journals Detecting spatiotemporal variability in the physicochemical properties of water in the Lower Mearim using remote sensing data

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
George Colares Silva Filho ◽  
Juliana Martins dos Santos ◽  
Paulo Cesar Mendes Villis ◽  
Ingrid Santos Gonçalves ◽  
Isael Coelho Correia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Physicochemical Properties ◽  
Monitoring Program ◽  
Remote Sensing Data ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Spatiotemporal Variability ◽  
Landsat 8 ◽  
Solids Content

Natural or anthropogenic chemical compounds of different origins often accumulate in estuarine regions. These compounds may alter the water quality. Therefore, It is important to constantly monitor the quality of estuarine regions. A combination of remote sensing and traditional sampling can lead to a better monitoring program for water quality parameters. The objective of this work is to assess the spatiotemporal variability of the physicochemical properties of water in the lower region of the Mearim River and estimate water quality parameters via remote sensing. Samples were collected at 16 points, from Baixo Arari to the mouth of the watershed, using a multiparameter meter and Landsat 8 satellite images. The physicochemical parameters of the water had high salinity levels, between 2.30 and 20.10 parts per trillion; a high total dissolved solids content, between 2.77 and 19.70 g/L; and minimum dissolved oxygen values. Estimating the physicochemical properties of the water via remote sensing proved feasible, particularly in the dry season when there is less cloud cover.

scholarly journals Effects of Water Quality Parameters on Abundance and Biomass of Zooplankton in Merbok Estuary Malaysia

2016 ◽  
Vol 9 (1) ◽  
pp. 117-122 ◽  
Author(s):  
K Fatema ◽  
WMW Omar ◽  
MM Isa ◽  
A Omar
Keyword(s):  
Water Quality ◽  
Light Intensity ◽  
Neap Tide ◽  
Spring Tide ◽  
Quality Parameters ◽  
Zooplankton Biomass ◽  
Water Quality Parameters ◽  
Zooplankton Abundance ◽  
Chl A ◽  
Merbok Estuary

Influence of water quality parameters on zooplankton abundance and biomass in the Merbok estuary Malaysia were investigated. Twenty four hours sampling were conducted at station 1, 3 and 5 from 12th November (spring tide) to 3rd December (neap tide) 2011 on weekly interval. Results showed that water quality parameters varied with the following ranges: conductivity (10.00-315.00?S-1cm), transparency (25.50-154.00 cm), light intensity (53.5-1959.00 lux), TSS (20-70 mg-1L), BOD (0.25-3.46 mg-1L) and chl a (0.1-1.46 ?g-1L). The highest zooplankton abundance was found at Station 5 (176×103) and (230×103) ind-3m and the lowest was at station 1(5.3×103) and (3.4 ×103) ind-3m during spring and neap tide. Zooplankton biomass varied from 0.04 to 0.096 gm-3m. Spearman’s rank correlation analysis results showed that there was a correlation among zooplankton abundance and conductivity, transparency, TSS, BOD, and biomass except chl and light intensity. Mann-Whitney U test result showed that water quality parameters, zooplankton abundance and zooplankton biomass were significantly different between spring and neap tides.J. Environ. Sci. & Natural Resources, 9(1): 117-122 2016

scholarly journals Prediction of Optical and Non-Optical Water Quality Parameters in Oligotrophic and Eutrophic Aquatic Systems Using a Small Unmanned Aerial System

Drones ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Juan G. Arango ◽  
Robert W. Nairn
Keyword(s):  
Water Quality ◽  
Multispectral Imaging ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
P Value ◽  
Unmanned Aerial System ◽  
Electromagnetic Spectrum ◽  
Linear Regression Models ◽  
Chl A ◽  
Average Value

The purpose of this study was to create different statistically reliable predictive algorithms for trophic state or water quality for optical (total suspended solids (TSS), Secchi disk depth (SDD), and chlorophyll-a (Chl-a)) and non-optical (total phosphorus (TP) and total nitrogen (TN)) water quality variables or indicators in an oligotrophic system (Grand River Dam Authority (GRDA) Duck Creek Nursery Ponds) and a eutrophic system (City of Commerce, Oklahoma, Wastewater Lagoons) using remote sensing images from a small unmanned aerial system (sUAS) equipped with a multispectral imaging sensor. To develop these algorithms, two sets of data were acquired: (1) In-situ water quality measurements and (2) the spectral reflectance values from sUAS imagery. Reflectance values for each band were extracted under three scenarios: (1) Value to point extraction, (2) average value extraction around the stations, and (3) point extraction using kriged surfaces. Results indicate that multiple variable linear regression models in the visible portion of the electromagnetic spectrum best describe the relationship between TSS (R2 = 0.99, p-value = <0.01), SDD (R2 = 0.88, p-value = <0.01), Chl-a (R2 = 0.85, p-value = <0.01), TP (R2 = 0.98, p-value = <0.01) and TN (R2 = 0.98, p-value = <0.01). In addition, this study concluded that ordinary kriging does not improve the fit between the different water quality parameters and reflectance values.

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