Air quality, water pollution, and radiation pollution are major factors that pose genuine challenges in the environment. Suitable monitoring is necessary so that the world can achieve sustainable growth, by maintaining a healthy society. In recent years, the environment monitoring has turned into a smart environment monitoring (SEM) system, with the advances in the internet of things (IoT) and the development of modern sensors. Under this scenario, the present manuscript aims to accomplish a critical review of noteworthy contributions and research studies on SEM, that involve monitoring of air quality, water quality, radiation pollution, and agriculture systems. The review is divided on the basis of the purposes where SEM methods are applied, and then each purpose is further analyzed in terms of the sensors used, machine learning techniques involved, and classification methods used. The detailed analysis follows the extensive review which has suggested major recommendations and impacts of SEM research on the basis of discussion results and research trends analyzed. The authors have critically studied how the advances in sensor technology, IoT and machine learning methods make environment monitoring a truly smart monitoring system. Finally, the framework of robust methods of machine learning; denoising methods and development of suitable standards for wireless sensor networks (WSNs), has been suggested.

https://www.mdpi.com/1424-8220/20/11/3113/pdf

Advances in Smart Environment Monitoring Systems Using IoT and Sensors

1Survey Department, Government of Nepal, Minbhawan, Kathmandu 44600, Nepal 2Department of Civil Engineering, Kangwon National University, 1 Kangdaehak-gil, Chuncheon 24341, Republic of Korea 3School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, No. 15 Yongyuan Road, Daxing District, Beijing 102616, China 4Institute of Transportation Studies, University of California Davis, 1605 Tilia Street, Davis, California 95616, USA

/pdf/comprehensive-review-on-application-of-machine-learning-28nty34in3.pdf

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Machine Learning Application in Water Quality Using Satellite Data

Water is one of the most vital sources for survival of life. In the globe, accessibility of water in a safety and healthy way are major concerns. Consumption of unsafe water may leads to health risk. Therefore, it is necessary to classify and monitor the quality of water, but main issue is sufficient parametric quality measures are not available with advanced technology. To overcome the above issue, this paper presents IoT based automated water quality monitoring system using cloud and machine learning algorithm. It contains various sensor devices like pH sensors, Temperature sensor, turbidity sensor, conductivity sensor. The classification of water quality in an accurate way is achieved by using fusion of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). The sensor values are generated and transferred in the cloud server via Node MCU with low power wide area networks (LPWAN). This proposed work can replace the classification and monitoring of traditional method to qualify the water status. It helps to save human being from various infections and diseases caused by unsafe usage of water. Water quality classification is very important to create eco-friendly environment. This proposed machine learning algorithm KNN+SVM is tested by 10-fold Cross Validation and the highest accuracy is 0.94 compared it with existing algorithm.

IoT based Automated Water Pollution Treatment using Machine Learning classifiers.

Algal blooms of the water are an important variable for the analysis of freshwater ecosystems, which are relevant not only for human populations but also for plant and animal diversity. Monitoring algal blooms from space allows for a continuous and automatic control without the necessity of water sampling and human intervention. However, it is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms. Water limnology, satellite imagery and neural networks can be used as an ensemble of remote sensing and machine learning technologies in order to estimate the concentration of algal blooms from space. This paper describes empirical algorithms to this end, which incorporate information from the multi-spectral instrument of Sentinel-2 satellite. This approach is applied to the Cefni Reservoir (Anglesey, U.K.), by using spatial and temporal scales. Algae estimation is accomplished using different types of neural and fuzzy neural networks and the experimental results are very accurate, therefore proving the reliability and accuracy of the proposed approach for monitoring water reservoirs by using remote sensing and neural networks tools.

Eutrophication Analysis of Water Reservoirs by Remote Sensing and Neural Networks

The construction of the Hydroelectric Power Plants has influenced the environment with their accompanying dams and reservoirs, resulting in changes to these ecosystems. Monitoring the reservoirs is important for the decision-making process. In this paper, we present different methodologies for estimating the concentration of Chlorophyll-a, transparency and total suspended solids in reservoir from remote sensing multispectral data, based on artificial neural networks and Leave-One-Out cross-validation. Simulations on different sample station of the study area were performed to determine a relationship between these physico-chemical parameters and the spectral response of the reservoir. Based on the in situ measurements, empirical models are established in order to relate the reservoir reflectance measured by Landsat 7 Enhanced TM+ with the water optical parameters. Four images for each year from 2007 to 2014 are calibrated and atmospherically corrected. Statistical analysis using error estimation is employed, aiming at evaluating the most accurate methodology. Artificial neural networks are trained by hydrological cycle and are shown to be useful in estimating the physico-chemical parameters of water from reflectance of visible and NIR bands of satellite images, with better results for the period with little rain and few clouds in the analyzed region.

Retrieving Chlorophyll-a levels, transparency and TSS concentration from multispectral satellite data by using artificial neural networks

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

H. A. Nascimento Silva

Author Tools

Chat about Author

Papers

Eutrophication Analysis of Water Reservoirs by Remote Sensing and Neural Networks

Retrieving Chlorophyll-a levels, transparency and TSS concentration from multispectral satellite data by using artificial neural networks