Classification of water quality status based on minimum quality parameters: application of machine learning techniques

2017 ◽  
Vol 4 (1) ◽  
pp. 311-324 ◽  
Author(s):  
Donya Dezfooli ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Kumars Ebrahimi ◽  
Shahab Araghinejad
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Quality Parameters ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Water Quality Status ◽  
Minimum Quality ◽  
Quality Status

scholarly journals Dissolved oxygen modeling of an urban stream using grid partitioning and subtractive clustering fuzzy techniques

2021 ◽  
Author(s):  
Sameer Arora
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Water Treatment ◽  
Dissolved Oxygen ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Machine Learning Techniques ◽  
Subtractive Clustering ◽  
Grid Partitioning ◽  
Learning Techniques

Dissolved oxygen is one of the prime parameters for assessing the water quality of any stream and the health status of aquatic life. The dissolved oxygen present in the water body plays an essential role in deciding water treatment processes to enhance water quality up to the design standards for the specified water use. Thus, the accurate estimation of dissolved oxygen concentration is necessary to evolve measures for maintaining the riverine ecosystem and designing the appropriate water treatment plants. Machine learning techniques are becoming useful tools for the prediction and simulation of water quality parameters. With these viewpoints, a study was carried out in the Delhi stretch of Yamuna River, India, and physiochemical parameters were examined for five years to simulate the dissolved oxygen using different machine learning techniques. Simulation and prediction competencies of ANFIS grid partitioning (ANFIS-GP) and ANFIS subtractive clustering (ANFIS-SC) were tested on various water quality parameters. Variation in dissolved oxygen was examined on various combinations of parameters. ANFIS-GP has been designed using the Gaussian function, and ANFIS-SC works on the likelihood of cluster centers. Results obtained from the models were evaluated using root mean square error (RMSE) and coefficient of determination (R2) to identify the optimum solution and appropriate combination of parameters that simulate the observed dissolved oxygen. Results of ANFIS-GP and ANFIS-SC indicate that both the models produce suitable solutions for the prediction; however, ANFIS-GP outperforms the ANFIS-SC and could act as a useful tool for defining, planning, and management of water quality parameters.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of sentence level sentiment analysis using cloud machine learning techniques

2017 ◽  
Vol 22 (S1) ◽  
pp. 1199-1209 ◽  
Author(s):  
R. Arulmurugan ◽  
K. R. Sabarmathi ◽  
H. Anandakumar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Sentence Level
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