Operation of storage reservoir for water quality by using optimization and artificial intelligence techniques

2004 ◽  
Vol 67 (4-5) ◽  
pp. 419-432 ◽  
Author(s):  
Paulo Chaves ◽  
Tsuneo Tsukatani ◽  
Toshiharu Kojiri
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Artificial Intelligence Techniques ◽  
Storage Reservoir

scholarly journals A Multidisciplinary Approach for Evaluating Spatial and Temporal Variations in Water Quality

Water ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 853 ◽  
Author(s):  
Viet Thang Le ◽  
Nguyen Hong Quan ◽  
Ho Huu Loc ◽  
Nguyen Thi Thanh Duyen ◽  
Tran Duc Dung ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Confusion Matrix ◽  
Temporal Variations ◽  
Machine Learning Techniques ◽  
Spatial And Temporal Variations ◽  
Support Vector ◽  
Domestic Water ◽  
Supply Source ◽  
Artificial Intelligence Techniques

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality.

scholarly journals Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1172
Author(s):  
Purushottam Agrawal ◽  
Alok Sinha ◽  
Satish Kumar ◽  
Ankit Agarwal ◽  
Ashes Banerjee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Traditional Approach ◽  
Human Life ◽  
Large Data ◽  
Quality Parameters ◽  
Support Vector ◽  
Artificial Intelligence Techniques

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life and civilization. The Water Quality Index is the most extensively used parameter for determining water quality worldwide. However, the traditional approach for the calculation of the WQI is often complex and time consuming since it requires handling large data sets and involves the calculation of several subindices. We investigated the performance of artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), and a support vector machine (SVM), for predicting the water quality index. We used an SVM and NBC for prediction, in conjunction with PSO for optimization. To validate the obtained results, groundwater water quality parameters and their corresponding water quality indices were found for water collected from the Pindrawan tank area in Chhattisgarh, India. Our results show that PSO–NBC provided a 92.8% prediction accuracy of the WQI indices, whereas the PSO–SVM accuracy was 77.60%. The study’s outcomes further suggest that ensemble machine learning (ML) algorithms can be used to estimate and predict the Water Quality Index with significant accuracy. Thus, the proposed framework can be directly used for the prediction of the WQI using the measured field parameters while saving significant time and effort.

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

SISTEM PAKAR MENDIAGNOSA VIRUS MAYORA DENGAN METODE VARIABLE CENTERED INTELLIGENT RULE SYSTEM (VCIRS)

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Siti Nurhena ◽  
Nelly Astuti Hasibuan ◽  
Kurnia Ulfa
Keyword(s):  
Artificial Intelligence ◽  
Immune System ◽  
Expert System ◽  
Expert Systems ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Visual Basic ◽  
Artificial Intelligence Techniques ◽  
Individual Needs ◽  
Rule System

The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms. Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms. With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)The diagnosis process is the first step to knowing a type of disease. This type of disease caused by mosquitoes is one of the major viruses (MAVY), dengue hemorrhagic fever (DHF) and malaria. Sometimes not everyone can find the virus that is carried by this mosquito, usually children who are susceptible to this virus because the immune system that has not been built perfectly is perfect. To know for sure which virus is infected by mosquitoes, it can diagnose by seeing symptoms perceived symptoms.Expert systems are one of the most used artificial intelligence techniques today because expert systems can act as consultations. In this case the authors make a system to start a diagnosis process with variable centered intelligent rule system (VCIRS) methods through perceived symptoms.With the facilities provided for users and administrators, allowing both users and administrators to use this system according to their individual needs. This expert system is made with the Microsoft Visual Basic 2008 programming language.Keywords: Expert System, Mayora Virus, Variable Centered Intelligent Rule System (VCIRS)

Use of Artificial Intelligence Techniques in the Prediction of the Pre and Asymptomatic Patient to COVID-19

2020 ◽  
Author(s):  
Efain Pantaleón-Matamoros ◽  
Isabelle Ribeiro Barbosa Mirabal ◽  
Orivaldo Vieira de Santana Júnior ◽  
Douglas de Souza Carvalho ◽  
Efrain Marcelo Pulgar Pantaleón ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Asymptomatic Patient ◽  
Artificial Intelligence Techniques

scholarly journals Using artificial intelligence techniques for COVID-19 genome analysis

2021 ◽  
Author(s):  
M. Saqib Nawaz ◽  
Philippe Fournier-Viger ◽  
Abbas Shojaee ◽  
Hamido Fujita
Keyword(s):  
Artificial Intelligence ◽  
Genome Analysis ◽  
Artificial Intelligence Techniques
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