Comment on “Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring A. Najah & A. El-Shafie & O. A. Karim & Amr H. El-Shafie. Environ Sci Pollut Res (2014) 21:1658-1670”

2015 ◽  
Vol 23 (1) ◽  
pp. 938-940 ◽  
Author(s):  
Taher Rajaee ◽  
Salar Khani
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Models ◽  
Water Quality Monitoring ◽  
Quality Monitoring

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring

2013 ◽  
Vol 21 (3) ◽  
pp. 1658-1670 ◽  
Author(s):  
A. Najah ◽  
A. El-Shafie ◽  
O. A. Karim ◽  
Amr H. El-Shafie
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Models ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

Study on Prediction Model of Dissolved Oxygen about Water Quality Monitoring System Based on BP Neural Network

2014 ◽  
Vol 912-914 ◽  
pp. 1407-1411 ◽  
Author(s):  
Jing Xin Yan ◽  
Li Juan Yu ◽  
Wen Wu Mao ◽  
Shou Qi Cao
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Monitoring System ◽  
Bp Neural Network ◽  
Water Quality Monitoring ◽  
High Water ◽  
Eriocheir Sinensis ◽  
Quality Monitoring

Eriocheir sinensis should cultivate in high water quality ponds, which is affected by many combined factors such as physics, chemistry, biology etc. Using the real-time water quality monitoring historical data to test one of the water quality indexes and predict this index in the next time has great significance. The dissolved oxygen is one of the most important indexes in aquaculture, such as in the Eriocheir sinensis pond. This paper established a dissolved oxygen prediction model of water quality monitoring system based on BP neural network. The forecast data which is predicted by the established model could fit the actual monitoring data very well.

scholarly journals Seasonality and Correlations between Water Quality Parameters in the Lower Danube at Chiciu for the Period 2010-2012

2020 ◽  
Vol 71 (2) ◽  
pp. 449-455
Author(s):  
Rodica-Mihaela Frincu ◽  
Cristian Omocea ◽  
Cerasela-Iuliana Eni ◽  
Eleonora-Mihaela Ungureanu ◽  
Olga Iulian
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Social Economic ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
The Other ◽  
Danube River ◽  
Quality Monitoring ◽  
The Social ◽  
Pollution Loads

The Danube River receives tributaries with different pollution loads, according to the social-economic characteristics of the adjacent regions. Water quality monitoring data from Chiciu, Calarasi county, Romania, for the three-year period (2010-2012), were analysed using statistical methods in order to identify correlations between parameters, as well as their evolution during the study period. The analysis has confirmedpositive correlations between nitrates and total nitrogen and between ortho-phosphates and total phosphorus. Negative correlations were found between water temperatures on one side and dissolved oxygen and nitrates on the other side. These parameters have a seasonal evolution, with high temperatures and low dissolved oxygen and nitrates levels during summer periods. Linear regression highlights decreasing nutrients pollution during the study period, which may be due to improved wastewater treatment along Danube tributaries.

scholarly journals Implementing Zigbee-based Wireless Sensor Network in the Design of Water Quality Monitoring System

2019 ◽  
Vol 8 (3) ◽  
pp. 6174-6179
Keyword(s):  
Water Quality ◽  
Wireless Sensor Network ◽  
Dissolved Oxygen ◽  
Water Level ◽  
Sensor Network ◽  
Water Quality Monitoring ◽  
Water Remediation ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Rf Transceiver

This study presents the design and development of a precision fishing technology utilized in water quality monitoring with phytoremediation system using a Zigbee-based Wireless Sensor Network. The system afforded a real-time water quality monitoring using multiple sensors spatially deployed. The sensor node implemented in the Wireless Sensor Network to perform data sensing utilities with the water quality parameters including the water temperature, pH level, water dissolved oxygen and the water level during high-tide and low-tide. During the development, a P89V51RD2 microcontroller, ZigBee module with IEEE 802.15.4 standard, and radio frequency (RF) transceiver were utilized. The developed precision fishing technology utilized the Internet of Things architecture. The IoT device layer includes the temperature sensor, pH sensor, dissolved oxygen sensor, and the water level sensor. Phytoremediation was also used as an alternative solution for soil and water remediation. Further studies using recent and advanced remote sensing technologies and IoT-based solutions can be developed to address issues in the primary sector of the economy.

Design and Fabrication of Thick Film Dissolved Oxygen Sensor Based on RuO2 Working Electrodes for Water Quality Monitoring

2018 ◽  
Vol 917 ◽  
pp. 59-63 ◽  
Author(s):  
Goib Wiranto ◽  
Slamet Widodo ◽  
I Dewa Putu Hermida ◽  
Roberth V. Manurung ◽  
Gandi Sugandi ◽  
...  
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Thick Film ◽  
Oxygen Sensor ◽  
Reference Electrode ◽  
Water Quality Monitoring ◽  
Input Voltage ◽  
Quality Monitoring ◽  
Step Response ◽  
Sensor Performance

A Dissolved Oxygen (DO) sensor has been designed and fabricated on an 8.5 x 22.5 mm Alumina substrate using thick film technology. The structure of the sensor device consisted of AgPd working/counter electrode, Ag/AgCl reference electrode, RuO2active layer, KCl electrolyte, and TiO2membrane. Formation of the Ag/AgCl reference electrode was done by chlorination of Ag layer using FeCl3, and the TiO2membrane was formed by screen printing of TiO2paste. Measurement was done to study the sensor’s performance based from the current-voltage characteristics between 1.1 – 1.6 V. The results showed that a stable diffusion current was obtained when the input voltage was 1.4 V, resulting in the best sensor performance with a sensitivity of 0.560 μA l/mg and a stable step response time of 4 min. The device showed highly potential to be used as candidate for online water quality monitoring system.

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