scholarly journals Estimation of Water Quality Index for Coastal Areas in Korea Using GOCI Satellite Data Based on Machine Learning Approaches

2016 ◽  
Vol 32 (3) ◽  
pp. 221-234 ◽  
Author(s):  
Eunna Jang ◽  
Jungho Im ◽  
Sunghyun Ha ◽  
Sanggyun Lee ◽  
Young-Gyu Park
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Satellite Data ◽  
Water Quality Index ◽  
Quality Index ◽  
Coastal Areas ◽  
Learning Approaches

scholarly journals Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices With Efficient Machine Learning Technique

2021 ◽  
Author(s):  
Jingjing Xia ◽  
Jin Zeng
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Resources ◽  
Environmental Factors ◽  
Prediction Model ◽  
Water Quality Index ◽  
Quality Index ◽  
Efficient Machine ◽  
Method Of Correlation

Abstract Water is an indispensable resource for human production and life. The evaluation of water quality by scientific method that provides sufficient support for the regeneration and recycling utilization of water resources. At present, water quality is mainly evaluated by water quality index (WQI) with weighted entropy value, which comprehensively considers the influence of different relevant environmental factors on the water quality. The calculation process is very complicated and time-consuming. In this paper, the method of correlation analysis is used to select the best combination of relevant environmental factors to assist the prediction model. Two typical kinds of machine learning methods are adopted and compared to realize the prediction of entropy water quality index (EWQI). After the better framework of prediction model is selected, four different kinds of optimization algorithms are used to optimize the prediction model to realize non-linear regression prediction and classification of water quality. According to the results of evaluation indicators, the framework of SVM is more suitable for realizing the prediction of EWQI. Meanwhile, the optimization algorithm of DE-GWO show great potential to improve the performance of SVM, which can make further contribution to the rational use and protection of water resources.

scholarly journals The Effect of Physical and Chemical Parameters on the Presence of Lompa Fish (Thryssa baelama Forsskål) in the Apui Coastal Waters of Central Maluku District

2017 ◽  
Vol 19 (2) ◽  
pp. 61 ◽  
Author(s):  
Meillisa Carlen Mainassy
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Good Condition ◽  
Environmental Water ◽  
Coastal Areas ◽  
Chemical Parameters ◽  
Ex Post ◽  
Physical And Chemical Parameters ◽  
Physical And Chemical

Maluku is one of the archipelago province in Indonesia that has small pelagic fish resources with widespread distribution, such as lompa (Thryssa baelama Forsskål). One of lompa habitat in Mollucas is in Apui coastal areas. The presence of lompa depends on physical and chemical parameters in the waters. This study aims to determine the influence of physical and chemical parameters on the presence of lompa in Apui coastal area Central Mollucas. This study was conducted in June - July 2015. This research is ex-post facto using the value of Environmental Water Quality Index which refers to US-National Sanitation Foundation-Water Quality Index (NSF-WQI). Physical and chemical parameters measured include temperature, velocity, depth, brightness, salinity, pH and dissolved oxygen. The results of calculations with the Indeks Mutu Lingkungan Perairan (IMLP) are 95.61; 92,15; 88.61; 96,37; 93,76; 79.57. And the results of lompa fishing were 181, 162, 205, 173, 184, and 97 respectively. The research conclusion is that the Apui coastal areas are in good condition and potential as lompa habitat.

scholarly journals Prediction of groundwater quality indices using machine learning algorithms

2021 ◽  
Author(s):  
Hemant Raheja ◽  
Arun Goel ◽  
Mahesh Pal
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Quality Indices ◽  
Extreme Gradient Boosting ◽  
Analysis Of Results

Abstract The present paper deals with performance evaluation of application of three machine learning algorithms such as Deep neural network (DNN), Gradient boosting machine (GBM) and Extreme gradient boosting (XGBoost) to evaluate the ground water indices over a study area of Haryana state (India). To investigate the applicability of these models, two water quality indices namely Entropy Water Quality Index (EWQI) and Water Quality Index (WQI) are employed in the present study. Analysis of results demonstrated that DNN has exhibited comparatively lower error values and it performed better in the prediction of both indices i.e. EWQI and WQI. The values of Correlation Coefficient (CC = 0.989), Root Mean Square Error (RMSE = 0.037), Nash–Sutcliffe efficiency (NSE = 0.995), Index of agreement (d = 0.999) for EWQI and CC = 0.975, RMSE = 0.055, NSE = 0.991, d = 0.998 for WQI have been obtained. From variable importance of input parameters, the Electrical conductivity (EC) was observed to be most significant and ‘pH’ was least significant parameter in predictions of EWQI and WQI using these three models. It is envisaged that the results of study can be used to righteously predict EWQI and WQI of groundwater to decide its potability.

scholarly journals Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Md. Mehedi Hassan ◽  
Md. Mahedi Hassan ◽  
Laboni Akter ◽  
Md. Mushfiqur Rahman ◽  
Sadika Zaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Efficient Prediction

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index

2020 ◽  
Vol 27 (33) ◽  
pp. 41524-41539 ◽  
Author(s):  
Sani Isah Abba ◽  
Quoc Bao Pham ◽  
Gaurav Saini ◽  
Nguyen Thi Thuy Linh ◽  
Ali Najah Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Ensemble Machine Learning ◽  
Intelligence Models

Machine learning approaches to coastal water quality monitoring using GOCI satellite data

2014 ◽  
Vol 51 (2) ◽  
pp. 158-174 ◽  
Author(s):  
Yong Hoon Kim ◽  
Jungho Im ◽  
Ho Kyung Ha ◽  
Jong-Kuk Choi ◽  
Sunghyun Ha
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Coastal Water ◽  
Satellite Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Learning Approaches ◽  
Coastal Water Quality

scholarly journals The first step of application of water quality index (WQI) to assessment of sea water quality in the Gulf of Tonkin in 2018

2021 ◽  
Vol 20 (4B) ◽  
pp. 171-181
Author(s):  
Le Van Nam ◽  
Tran Duc Thanh ◽  
Nguyen Van Thao ◽  
Dang Hoai Nhon ◽  
Le Xuan Sinh ◽  
...  
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Sea Water ◽  
Coastal Areas ◽  
Island Area ◽  
Poor Water Quality ◽  
Calculation Results ◽  
Gulf Of Tonkin ◽  
Average Water

The study to calculate the water quality index was conducted in the Gulf of Tonkin at the surface water in August 2018. The calculation results showed that out of 48 surveyed points, there was 1 point in the Northeast area at poor water quality, 15 points in the Northeast and coastal areas from Thanh Hoa to Thua Thien Hue at medium water quality, 14 points in the Gulf of Tonkin, Northeast and Con Co Island at good water quality, the remaining 18 points in the Gulf of Tonkin and Bach Long Vi Island at excellent water quality. Overall, the average water quality of the whole region was good (average WQI = 79). Considering each area, the Gulf of Tonkin area had good and excellent water quality, the Northeast had water quality from poor to good level, the coastal areas from Thanh Hoa to Thua Thien Hue had medium water quality, Con Co Island area had good water quality, Bach Long Vi Island area had excellent water quality.

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