Assessment of water quality index in unmonitored river basin using multilayer perceptron neural networks and principal component analysis

2020 ◽  
Vol 200 ◽  
pp. 42-54
Author(s):  
Bachir Sakaa ◽  
Nabil Brahmia ◽  
Hicham Chaffai ◽  
Azzedine Hani
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Multilayer Perceptron ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis

scholarly journals Water quality index and hydrogeochemistry of Otukpo area, Benue State, North Central Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. 4-14
Author(s):  
Buba Apagu Ankidawa ◽  
Ujah Linus Sunday ◽  
Ibrahim Vanke
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Samples ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Research Area ◽  
Water Type ◽  
Surface And Groundwater

The research is aimed to assess the surface and groundwater quality in Otukpo area and environs, Benue State, North Eastern Nigeria. Sixteen water samples were collected from 7 boreholes, 7 hand duck wells and 2 rivers. The water samples were analysed chemically and bacteriologically using spectrophotometric, titrimetric and membrane filtration methods. Analytical results indicated that the groundwater in the area is acidic, fresh and moderately hard. The order of abundance of the cations were in Na+<K+<Mg2+<Ca2+ while the anions were in the order of Cl-<HCO3->SO42-<NO3-. Principal Component Analysis (PCA) identified four factors that accounts for 69.73% of the total variance. Correlation analysis, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) revealed pollution from application of agricultural fertilizers, anthropogenic contamination and rock-water interaction as the major processes responsible for the modification of surface and groundwater chemistry of the research area. The Gibbs diagram plot shows that, the sample points fall under rock dominance and weathering zones, which suggested precipitation, induced chemical weathering with the dissolution of rock-forming minerals. The piper diagram classified groundwater samples as Ca-Mg-HCO3 water type. Water Quality Index (WQI) values range from 22.05 to 56.13 which indicated good and excellent water category. The SAR values range from 0.02 to 0.66 the values belong to the excellent category and is suitable for irrigation. The overall result revealed that, the water in the research area is suitable for domestic, industrial and irrigation activities.

Assessment of Aquatic Ecological Health: A Case Study of Lake Rewalsar, Himachal Pradesh

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
RAMA KUMARI ◽  
PARMANAND KUMAR
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Index ◽  
Quality Index ◽  
Management Strategies ◽  
Oxygen Demand ◽  
Principal Component ◽  
Component Analysis ◽  
Different Seasons

The present study was conducted for two years to analyze the water quality of the sacred lake Rewalsar. Water quality of different seasons was evaluated by water quality index. Various statistical techniques, such as correlation, principal component analysis were applied. Based on Water Quality Index, water quality of the lake was in the range of 33-80 in different seasons. Cluster analysis of similarity indicates the relationship intensity between the seasons as cluster ranged 80-100% during the study period. In the principal component analysis maximum variables (Conductivity, Alkalinity, Biochemical Oxygen Demand, Nitrates, Phosphates, and Chloride) shows maximum influence during the summer and monsoon. The outcome revealed that the major driving factors of water quality deterioration are the runoff of effluent from the domestic area and offering food materials to the fishes. So, it is necessary to implement effective management strategies for the conservation of the Rewalsarlake.

scholarly journals Robust principal component analysis in water quality index development

2014 ◽  
Author(s):  
Zalina Mohd Ali ◽  
Noor Akma Ibrahim ◽  
Kerrie Mengersen ◽  
Mahendran Shitan ◽  
Hafizan Juahir
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Robust Principal Component Analysis ◽  
Index Development

scholarly journals Application of Principal Component Analysis for Developing Water Quality Index for Selected Coastal Areas of Alexandria Egypt

2012 ◽  
Vol 2 (6) ◽  
pp. 297-305 ◽  
Author(s):  
Shadia A. H. Fathy ◽  
Fatma F. Abdel Hamid ◽  
Mohamed A. Shreadah ◽  
Laila A. Mohamed ◽  
Mohamed G. El-Gazar
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Coastal Areas

scholarly journals The Langat River water quality index based on principal component analysis

2013 ◽  
Author(s):  
Zalina Mohd Ali ◽  
Noor Akma Ibrahim ◽  
Kerrie Mengersen ◽  
Mahendran Shitan ◽  
Hafizan Juahir
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
River Water Quality ◽  
Langat River

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

scholarly journals Using Principal Component Scores and Artificial Neural Networks in Predicting Water Quality Index

10.5772/32577 ◽  
2012 ◽  
Author(s):  
Rashid Atta ◽  
Sharifuddin M. ◽  
Hafizan Juahir ◽  
Mohd Kamil ◽  
Tg Hanidza
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Artificial Neural ◽  
Component Scores
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