Applying Machine Learning Techniques to Evaluate Water Quality in Reservoirs

2019 ◽  
Author(s):  
Jui-Sheng Chou ◽  
Ha-Son Hoang
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Machine Learning Techniques ◽  
Learning Techniques

Classification Model for Water Quality using Machine Learning Techniques

2015 ◽  
Vol 9 (6) ◽  
pp. 45-52 ◽  
Author(s):  
Salisu Yusuf Muhammad ◽  
Mokhairi Makhtar ◽  
Azilawati Rozaimee ◽  
Azwa Abdul Aziz ◽  
Azrul Amri Jamal
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Water quality monitoring: from conventional to emerging technologies

2019 ◽  
Vol 20 (1) ◽  
pp. 28-45
Author(s):  
Umair Ahmed ◽  
Rafia Mumtaz ◽  
Hirra Anwar ◽  
Sadaf Mumtaz ◽  
Ali Mustafa Qamar
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Industrial Development ◽  
Low Cost ◽  
Research Work ◽  
Water Quality Monitoring ◽  
Machine Learning Techniques ◽  
Rapid Urbanization ◽  
Analysis Process ◽  
Learning Techniques

Abstract The rapid urbanization and industrial development have resulted in water contamination and water quality deterioration at an alarming rate, deeming its quick, inexpensive and accurate detection imperative. Conventional methods to measure water quality are lengthy, expensive and inefficient, including the manual analysis process carried out in a laboratory. The research work in this paper focuses on the problem from various perspectives, including the traditional methods of determining water quality to gain insight into the problem and the analysis of state-of-the-art technologies, including Internet of Things (IoT) and machine learning techniques to address water quality. After analyzing the currently available solutions, this paper proposes an IoT-based low-cost system employing machine learning techniques to monitor water quality in real time, analyze water quality trends and detect anomalous events such as intentional contamination of water.

scholarly journals Comparison of Machine Learning Algorithms for Retrieval of Water Quality Indicators in Case-II Waters: A Case Study of Hong Kong

2019 ◽  
Vol 11 (6) ◽  
pp. 617 ◽  
Author(s):  
Sidrah Hafeez ◽  
Man Wong ◽  
Hung Ho ◽  
Majid Nazeer ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Satellite Data ◽  
Coastal Waters ◽  
Machine Learning Techniques ◽  
Blue Band ◽  
Chl A ◽  
Learning Techniques ◽  
Reflectance Data

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

scholarly journals Machine learning techniques in river water quality modelling: a research travelogue

2020 ◽  
Author(s):  
Sakshi Khullar ◽  
Nanhey Singh
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Resources ◽  
River Water ◽  
Water Quality Modeling ◽  
River Water Quality ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Aquatic Life ◽  
Learning Techniques

Abstract Water is a prime necessity for the survival and sustenance of all living beings. Over the past few years, the water quality of rivers has been adversely affected due to harmful wastes and pollutants. This ever-increasing water pollution is a matter of great concern as it is deteriorating the water quality, making it unfit for any type of use. Contaminated water resources can cause serious effects on humans as well as aquatic life. Hence, water quality monitoring of reservoirs is essential. Recently, water quality modeling using AI techniques has generated a lot of interest and it can be very beneficial in ecological and water resources management. This paper presents the state-of-the-art application of machine learning techniques in forecasting river water quality. It highlights the different key techniques, advantages, disadvantages, and applications with respect to monitoring the river water quality. The review also intends to find the existing challenges and opportunities for future research.

scholarly journals A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality

2021 ◽  
Vol 18 (21) ◽  
pp. 351
Author(s):  
Al-Akhir Nayan ◽  
Joyeta Saha ◽  
Ahamad Nokib Mozumder ◽  
Khan Raqib Mahmud ◽  
Abul Kalam Al Azad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Early Detection ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Fish Diseases ◽  
Natural Processes ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Underlying Causes

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak and thus to avert financial losses with apparent negative implications to the national economy. Typically, fish diseases are caused by viruses and bacteria; according to biochemical studies, the presence of certain bacteria and viruses may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition, also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. Thus, it helps to take preemptive steps against potential fish diseases. The experimental results show high accuracy in detecting fish diseases specific to water quality based on the algorithm with real datasets.

Desktop Application for Water Quality Prediction and Monitoring System Using ISO 9241-210 and Machine Learning Techniques

2021 ◽  
pp. 44-57
Author(s):  
Maximiliano Guzman-Fernandez ◽  
Huizilopoztli Luna-García ◽  
Cesar A. Collazos ◽  
Wilson J. Sarmiento ◽  
Jorge I. Galvan-Tejada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Monitoring System ◽  
Machine Learning Techniques ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Learning Techniques ◽  
Desktop Application
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