An Intelligent System for Monitoring and Predicting Water Quality” (original) (raw)
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Monitoring water quality through a telematic sensor network and a fuzzy expert system
Expert …, 2007
In this paper we present an expert system that monitors sea water quality and pollution in Northern Greece, through a sensor network called "Andromeda". The expert system monitors sensor data collected by Local Monitoring Stations and reasons about the current level of water suitability for various aquatic uses, such as swimming and piscicultures. The aim of the expert system is to help the authorities in the "decisionmaking" process in the battle against the pollution of the aquatic environment, which is very vital for the public health and the economy of Northern Greece. The expert system determines, using fuzzy logic, when certain environmental parameters exceed certain "pollution" limits, which are specified either by the authorities or by environmental scientists, and flags out appropriate alerts.
Intelligent System for Monitoring and Detecting Water Quality
Testing water quality has a significant role in environment controlling. Whenever, the water quality is bad it can affect the aquatic life and surrounding environment. Due to the importance of some parameters to show the quality of water, we have designed an intelligent system that can measures remotely five parameters of water. The captured values are sent to the database which is connected to the platform. The platform can process the received values. The user can connect to the application via Internet Protocol for monitoring the measured parameters. The outcomes demonstrate that with fitting alignment, a dependable observing framework can be built up. This will enable catchment administrators to consistently observing the nature of the water at higher spatial goals than has recently been doable, and to keep up this reconnaissance over an all-inclusive timeframe. Moreover, it comprehends the conduct of seagoing creatures in respect to water contamination utilizing information investigation.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
The sensor innovation for water quality checking (WQM) has improved during late years. The financially savvy sensorised instruments that can independently gauge the fundamental physical-substance and organic (PCB) factors are presently promptly accessible and are being sent on floats, boats and ships. However, there is a distinction between the information quality, information gathering and information examination because of the absence of normalized approaches for information assortment and handling, spatio-fleeting variety of key boundaries in water bodies and new pollutants. Such holes can be spanned with an organization of multiparametric sensor frameworks sent in water bodies utilizing independent vehicles, for example, marine robots and flying vehicles to widen the information inclusion in existence. Further, smart calculations could be utilized for normalized information examination what's more, determining. This paper presents an exhaustive survey of the sensors, sending and examination advancements for WQM. An organization of arranged water bodies could improve the worldwide information interoperability and empower WQM at worldwide scale to address worldwide difficulties connected with food, drinking water, and wellbeing.
SmartCoasts is an INTERREG 4A project aimed at providing novel solutions for real-time monitoring and forecasting of coastal water quality. The intended predictive system relies on freely available online weather forecasts and a suite of real-time meteorological data measured across a river catchment. In a preliminary stage, a prototype has been developed taking the real-time data from GPRS loggers deployed at strategically located stations according to a centralised architecture. Even though such system has proven its suitability providing accurate predictions, certain pitfalls that hamper usability have been detected. Adding intelligent capabilities to the sensing nodes might help to overcome such situation. This paper presents a general overview of the current situation and discusses some of the major challenges and difficulties that need to be faced in order to set up a really smart Environmental Wireless Sensor Network.
Intelligent model for predicting water quality
INTERNATIONAL JOURNAL OF ADVANCE RESEARCH, IDEAS AND INNOVATIONS IN TECHNOLOGY
Over the decades, water pollution has been a real threat to the living species. The real-time monitoring of drinking water is nothing less than a challenging task. This paper aims to design and develop a low-cost system for the real-time monitoring of water quality using the Internet of Things (IoT) and Machine Learning (ML). The physical and chemical parameters of water such as temperature, level, moisture, humidity and visibility are measured using respective sensors. ESP8266, the core controller is employed to process the measured values from the sensors. The data acquired from Sensors are sent to the Django server. Random Forest (RF) and K-Nearest Neighbours (KNN) algorithm are used in the analysis and prediction of water quality.
Water Quality Monitoring Systems Based on Intelligent Agents: A Systematic Literature Review
Research in Computing Science, 2018
Water is a vital resource for life; however, although most of the planet is covered with water, only a small percentage corresponds to fresh water. Also, a low percentage of fresh water corresponds to drinking water, that is, water useful for human consumption. Despite this, the society has been contaminating the sources of fresh water, reducing more and more the amount of water available for human use. This paper presents a systematic literature review on the various modern mechanisms to monitor water quality, through the use of technology to take measurements and the use of storage devices. A comparison is made between studies regarding in this topic and a set of stands of work is proposed to be developed in the future. This review may guide the reader about the basis of available of information and communication technologies and their application in management systems to monitor water quality, in order to facilitate the search for planning and design of sustainable new system.
Ai in Support to Water Quality Monitoring
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021
This study explores the possibility of using Artificial Intelligence (AI) as a means to support water monitoring. More precisely, it addresses the issue of the quality and reliability of Citizen Science data. The paper addresses the tools and data of the SIMILE (Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) project in order to develop an open prefiltering system for Volunteer Geographic Information (VGI) of lake water monitoring at the global scale. The goal is to automatically determine the presence of harmful phenomena (algae and foams) in the images uploaded by citizen scientists to reduce the time required for a manual check of the contributions. The task is challenging because of the heterogeneity of the data that consist in geotagged pictures taken without specific instructions. For this purpose, different tools and deep learning techniques have been tested (Clarifai platform, a Convolutional Neural Network (CNN), and an object detection algorithm called faster Region-based CNN (R-CNN). The original dataset composed by the observations of SIMILE-Lake Monitoring application, has been integrated with the results of both keyword and image searches on web engines (Google, Bing, etc) and crawling Flickr data. The performances of the different algorithms are presented for their capability of detecting the presence and correctly labelling the phenomenon together with some possible strategies to improving them in the future.
IJERT-Real-Time Water Quality Monitoring System
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/real-time-water-quality-monitoring-system https://www.ijert.org/research/real-time-water-quality-monitoring-system-IJERTCONV8IS15032.pdf Nowadays water is the is the most valuable for all the human beings drinking water utilities faces challenges in real-time operation. These challenges occurred because of growing population, limited water resources, ageing infrastructure etc. Hence there is a need of better methodologies for monitoring the water quality. To reduce the water relateddiseases and prevent water populationWorld health Organization (WHO) has also stated thiscrisis as "the largest mass poisoning of a population in history".The main goal of this paperto build a Sensor-based Water Quality Monitoring System.
Hydrology
The monitoring of surface waters is of fundamental importance for their preservation under good quantitative and qualitative conditions, as it can facilitate the understanding of the actual status of water and indicate suitable management actions. Taking advantage of the experience gained from the coordination of the national water monitoring program in Greece and the available funding from two ongoing infrastructure projects, the Institute of Inland Waters of the Hellenic Centre for Marine Research has developed the first homogeneous real-time network of automatic water monitoring across many Greek rivers. In this paper, its installation and maintenance procedures are presented with emphasis on the data quality checks, based on values range and variability tests, before their online publication and dissemination to end-users. Preliminary analyses revealed that the water pH and dissolved oxygen (DO) sensors and produced data need increased maintenance and quality checks respectively...
Field Study of Real-Time Water Quality Control
Water and Society IV, 2017
The objective of a drinking water network is to provide a good quality of water to users. Accidental or intentional contamination can degrade the water quality and consequently threats the consumer's health. Generally, the water quality is monitored using traditional methods, based on manual sampling, which can take several days. Early warning of water contamination can be achieved using smart technology. This paper presents a field study of the use of this technology in real-time monitoring of the water quality. The field study is conducted at the Campus of the University of Lille in the North of France within the European Project "SmartWater4Europe" (http://www.sw4eu.com). Two sensors are installed in the campus: S::CAN and EventLab which measure several water quality parameters such as TOC (Total Organic Carbon), turbidity, refractive index, etc. This paper presents analysis of these parameters as well as the influence of hydraulic parameters on the water quality. It presents also an event detection system, which is developed using CANARY software. A sensitivity study is presented to determine the appropriate parameters in order to reduce false alarms and to determine the probability of possible event.