Water quality of Danube Delta systems: ecological status and prediction using machine-learning algorithms

2016 ◽  
Vol 73 (10) ◽  
pp. 2413-2421
Author(s):  
C. Stoica ◽  
J. Camejo ◽  
A. Banciu ◽  
M. Nita-Lazar ◽  
I. Paun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Real Time ◽  
Ecological Status ◽  
Environmental Issues ◽  
Machine Learning Algorithms ◽  
Water Bodies ◽  
Coliform Bacteria ◽  
Current Status ◽  
Danube Delta

Environmental issues have a worldwide impact on water bodies, including the Danube Delta, the largest European wetland. The Water Framework Directive (2000/60/EC) implementation operates toward solving environmental issues from European and national level. As a consequence, the water quality and the biocenosis structure was altered, especially the composition of the macro invertebrate community which is closely related to habitat and substrate heterogeneity. This study aims to assess the ecological status of Southern Branch of the Danube Delta, Saint Gheorghe, using benthic fauna and a computational method as an alternative for monitoring the water quality in real time. The analysis of spatial and temporal variability of unicriterial and multicriterial indices were used to assess the current status of aquatic systems. In addition, chemical status was characterized. Coliform bacteria and several chemical parameters were used to feed machine-learning (ML) algorithms to simulate a real-time classification method. Overall, the assessment of the water bodies indicated a moderate ecological status based on the biological quality elements or a good ecological status based on chemical and ML algorithms criteria.

scholarly journals Efficient Water Quality Prediction Using Supervised Machine Learning

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2210 ◽  
Author(s):  
Umair Ahmed ◽  
Rafia Mumtaz ◽  
Hirra Anwar ◽  
Asad A. Shah ◽  
Rabia Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Real Time ◽  
Polynomial Regression ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Rapid Urbanization ◽  
Water Quality Prediction

Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.

scholarly journals Retrieval of Water Quality from UAV-Borne Hyperspectral Imagery: A Comparative Study of Machine Learning Algorithms

2021 ◽  
Vol 13 (19) ◽  
pp. 3928
Author(s):  
Qikai Lu ◽  
Wei Si ◽  
Lifei Wei ◽  
Zhongqiang Li ◽  
Zhihong Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Learning Algorithms ◽  
Quality Parameters ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
World Population ◽  
Freshwater Resources

The rapidly increasing world population and human activities accelerate the crisis of the limited freshwater resources. Water quality must be monitored for the sustainability of freshwater resources. Unmanned aerial vehicle (UAV)-borne hyperspectral data can capture fine features of water bodies, which have been widely used for monitoring water quality. In this study, nine machine learning algorithms are systematically evaluated for the inversion of water quality parameters including chlorophyll-a (Chl-a) and suspended solids (SS) with UAV-borne hyperspectral data. In comparing the experimental results of the machine learning model on the water quality parameters, we can observe that the prediction performance of the Catboost regression (CBR) model is the best. However, the prediction performances of the Multi-layer Perceptron regression (MLPR) and Elastic net (EN) models are very unsatisfactory, indicating that the MLPR and EN models are not suitable for the inversion of water quality parameters. In addition, the water quality distribution map is generated, which can be used to identify polluted areas of water bodies.

scholarly journals Assessment of the Impact of Small Hydropower Plants on the Ecological Status Indicators of Water Bodies: A Case Study in Lithuania

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 433
Author(s):  
Laima Česonienė ◽  
Midona Dapkienė ◽  
Petras Punys
Keyword(s):  
Water Quality ◽  
Benthic Macroinvertebrates ◽  
Suspended Solids ◽  
Ecological Status ◽  
Fish Abundance ◽  
Water Bodies ◽  
Small Hydropower ◽  
Hydropower Plants ◽  
Physico Chemical ◽  
The Impact

Hydropower plants produce renewable and sustainable energy but affect the river’s physico-chemical characteristics and change the abundance and composition of the aquatic organisms. The impact of large HPPs on the ecological conditions of surface water bodies have been extensively studied, but less attention has been paid to environmental impact studies of small hydropower plants (SHPs). The impact of hydropeaking on both the river flow regime and ecosystems has been well-studied for peaking mode plants, mainly medium to large-sized ones. However, for small hydroelectric power plants, and especially for those in lowland rivers, the available information on water quality, benthic macroinvertebrates communities and fish abundance, and biomass is not sufficient. Ten small hydropower plants were selected, and the ecological status of water bodies was assessed in different parts of Lithuania. The studies were performed at the riverbed upstream from the SHPs, where the hydrological regime has not changed, and downstream from the SHPs. It was found that the small hydropower plants do not affect the physico-chemical values of the water quality indicators. This study demonstrated that the total number of benthic macroinvertebrates taxa (TS) is influenced by the concentration of nitrogen and suspended solids, the water flow, the river area, and the current speed; the number of EPT (Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies)) taxa is influenced by the concentration of nitrogen and suspended solids. The studied indicators do not have a significant impact on biomass. The SHPs affect the fish abundance and biomass. The Lithuanian fish index (LFI) is influenced by the average depth and area of the river. Some SHPs operating in lowland areas may yield somewhat significant hydrograph ramping but more detailed investigation is needed to support the significance of this impact on the biological indices.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

<p>Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R<sup>2</sup> = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R<sup>2</sup> = 0.75 and 0.78 respectively) while DL performs better in TDS (R<sup>2</sup> =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.</p>

scholarly journals False Positive RFID Detection Using Classification Models

2019 ◽  
Vol 9 (6) ◽  
pp. 1154 ◽  
Author(s):  
Ganjar Alfian ◽  
Muhammad Syafrudin ◽  
Bohan Yoon ◽  
Jongtae Rhee
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Real Time ◽  
Outlier Detection ◽  
Radio Frequency Identification ◽  
False Positives ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Automated Identification ◽  
Rfid Data

Radio frequency identification (RFID) is an automated identification technology that can be utilized to monitor product movements within a supply chain in real-time. However, one problem that occurs during RFID data capturing is false positives (i.e., tags that are accidentally detected by the reader but not of interest to the business process). This paper investigates using machine learning algorithms to filter false positives. Raw RFID data were collected based on various tagged product movements, and statistical features were extracted from the received signal strength derived from the raw RFID data. Abnormal RFID data or outliers may arise in real cases. Therefore, we utilized outlier detection models to remove outlier data. The experiment results showed that machine learning-based models successfully classified RFID readings with high accuracy, and integrating outlier detection with machine learning models improved classification accuracy. We demonstrated the proposed classification model could be applied to real-time monitoring, ensuring false positives were filtered and hence not stored in the database. The proposed model is expected to improve warehouse management systems by monitoring delivered products to other supply chain partners.

scholarly journals Knowledge discovery in Chinese herbal medicine: a machine learning perspective

2021 ◽  
Vol 336 ◽  
pp. 06024
Author(s):  
Nan Liang ◽  
Qing Liang ◽  
Fenglei Ji
Keyword(s):  
Machine Learning ◽  
Herbal Medicine ◽  
Knowledge Discovery ◽  
Chinese Herbal Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Current Status ◽  
Network Pharmacology ◽  
Valuable Insight ◽  
Chinese Herbal

Traditional Chinese Medicine (TCM) has attracted more and more attention due to its remarkable effects on treating diseases, and Chinese herbal medicine (CHM) is an important partition of TCM, rich in natural active ingredients. Researchers are trying multiple analytical methods to dig out more valuable information about CHM and reveal the principle of TCM. Machine learning is playing an important role in the studies. Knowledge discovery of CHM using machine learning mainly includes quality control of CHM, network pharmacology in CHM, and medical prescriptions composed by CHM, aiming to understand TCM better, provide more efficiency methods in the production of CHM and find novel treatment of disease not curable nowadays. In this paper, we summarized the basic idea of frequently used classification and clustering machine learning algorithms, introduced pre-processing algorithms commonly used to simplify and accelerate machine learning procedure, presented current status of machine learning algorithms’ applications in knowledge discovery of CHM, discussed challenges and future trends of machine learning’s application in CHM. It is believed that the paper provides a valuable insight for the starters trying to apply machine learning in the study of CHM and catch up the recent status of related researches.

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