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A generalized machine learning approach for dissolved oxygen estimation at multiple spatiotemporal scales using remote sensing.

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TLDR
In this paper, support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements and water temperature of Lake Huron and three other inland waterbodies.
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This article is published in Environmental Pollution.The article was published on 2021-11-01. It has received 15 citations till now. The article focuses on the topics: Moderate-resolution imaging spectroradiometer.

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Performance of deep learning in mapping water quality of Lake Simcoe with long-term Landsat archive

TL;DR: In this paper , a multimodal deep learning (MDL) model was developed and rigorously validated using atmospherically corrected Landsat remote sensing reflectance data and synchronous water quality measurements for estimating long-term Chlorophyll-a (Chl-a ), total phosphorus (TP), and total nitrogen (TN) in Lake Simcoe, Canada.
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Dissolved oxygen concentration predictions for running waters with using hybrid machine learning techniques

TL;DR: In this paper, support vector regression (SVR) was employed for prediction of the model in both standalone and hybrid forms, which consisted in SVR combined with metaheuristic algorithms of chicken swarm optimization (CSO), social ski-driver (SSD) optimization, Black widow optimization (BWO), and the Algorithm of the innovative gunner (AIG).
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The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm.

TL;DR: In this article , the authors employed GA for optimization of the selection and layout of low impact development (LID) facilities and layouts for a sub-catchment is important for designing stormwater management system.
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Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

TL;DR: Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner as discussed by the authors .
References
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Journal ArticleDOI

Overview of Intercalibration of Satellite Instruments

TL;DR: Use of a robust set of internationally agreed upon and coordinated intercalibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth's climate at uncertainty levels needed to detect and attribute the mechanisms of change.
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Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing

TL;DR: In this paper, a review of water quality remote sensing systems and their limitations is presented, and the authors conclude that anomaly detection utilizing multi-sensor data fusion and virtual constellation in cloud computing is the most promising means for predicting impending water pollution outbreaks such as algal blooms.
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Water quality change in reservoirs of Shenzhen, China: detection using LANDSAT/TM data

TL;DR: Water quality of most reservoirs have become worse and satellite-based estimates and in situ measured water reflectance have very high correlation, and the root mean square differences between two kinds of indices are close to 0.02-0.03.

Remote sensing of suspended sediments in surface waters

TL;DR: This article measured reflected and incident solar radiation 20 to 50 cm above the water surface in six northern Mississippi reservoirs between August 1973 and December 1974 and showed that the relationship between reflected solar radiation, or reflectance, and suspended sediment concentration of surface water was between 700 and 800 nm.
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Improving prediction of water quality indices using novel hybrid machine-learning algorithms.

TL;DR: Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQIsc, and all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.
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