scholarly journals Pengenalan Aksara Batak Dengan Metode Perceptron

2019 ◽  
Vol 4 (1) ◽  
pp. 160 ◽  
Author(s):  
Frinto Tambunan
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Contaminated Water ◽  
Artificial Neural ◽  
Impaired Water ◽  
Pollution Prediction

Water is a good solvent, so that the water in nature is never pure. If the content of various substances and microorganisms contained in water exceeds the allowable threshold, the water quality will be compromised. Impaired water quality is said to be contaminated water. With so keep in mind how the level of contamination found in the water. With the construction of an artificial neural network using the Perceptron network is expected to be built capable of providing the level of water pollution prediction results.

scholarly journals Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis

2021 ◽  
Vol 13 (2) ◽  
pp. 792
Author(s):  
Zheng Zeng ◽  
Wei-Ge Luo ◽  
Zhe Wang ◽  
Fa-Cheng Yi
Keyword(s):  
Neural Network ◽  
Water Pollution ◽  
Water Quality ◽  
Artificial Neural Network ◽  
River Basin ◽  
Back Propagation ◽  
Oxygen Demand ◽  
Ammonia Nitrogen ◽  
National Standards ◽  
Artificial Neural

This work aimed to assess the water quality of the Tuojiang River Basin in recent years to provide a better understanding of its current pollution situation, and the potential pollution risks and causes. Water quality parameters such as dissolved oxygen (DO), ammonia–nitrogen (NH3-N), total phosphorus (TP), the permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), pH, and concentrations of various heavy metals were measured in the Tuojiang River, according to the national standards of the People’s Republic of China. Samples were collected between 2012 to 2018 at 11 national monitoring sites in the Tuojiang River Basin. The overall water pollution situation was evaluated with back propagation artificial neural network (BP-ANN) analysis. The pollution causes were analyzed considering both industrial wastewater discharge in the upper reaches and the current pollution situation. We found potential risks of excessive NH3-N, TP, Cd, Hg, and Pb concentrations in the Tuojiang River Basin. Moreover, corresponding water pollution control suggestions were given.

scholarly journals Prediction of Water Quality based on artificial neural network

2021 ◽  
Vol 1738 ◽  
pp. 012066
Author(s):  
Yingjia Wu ◽  
Rong Ling ◽  
Jixian Zhou ◽  
Mengxin Zhang ◽  
Wei Gao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Artificial Neural

scholarly journals Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5875
Author(s):  
Monika Kulisz ◽  
Justyna Kujawska ◽  
Bartosz Przysucha ◽  
Wojciech Cel
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Water Quality Index ◽  
Quality Index ◽  
Network Models ◽  
Multiple Regression Model ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.

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