research-article

Open access

Evaluating the use of machine learning algorithms in environmental sensing for energy saving

Authors:

Giovanni Delnevo,

Gianni Tumedei,

Vittorio Ghini,

Catia PrandiAuthors Info & Claims

NET4us '23: Proceedings of the 2nd Workshop on Networked Sensing Systems for a Sustainable Society

Pages 201 - 206

https://doi.org/10.1145/3615991.3616402

Published: 06 October 2023 Publication History

Abstract

Coastal lagoons are complex ecosystems characterized by the interaction of several actors, that can have a significant impact on them. The SMARTLAGOON project has the primary aim of integrating novel artificial intelligence-based technologies with an efficient Internet of Things (IoT) sensing infrastructure in the Mar Menor coastal lagoon. This paper presents an approach to predict some variables (chlorophyll and turbidity) usually sensed by the smart bouy in future instants of time. Results show that machine learning algorithms can accurately predict them.

References

[1]

Ana C. Brito, Alice Newton, Paul Tett, and Teresa F. Fernandes. 2012. How will shallow coastal lagoons respond to climate change? A modelling investigation. Estuarine, Coastal and Shelf Science 112 (2012), 98--104. Assessing Ecological Quality in Estuarine and Coastal Systems - Functional Perspective.

[2]

José M. Cecilia, Pietro Manzoni, Dennis Trolle, Anders Nielsen, Pablo Blanco, Catia Prandi, Salvador Peña Haro, Line Barkved, Don Pierson, and Javier Senent. 2021. SMARTLAGOON: Innovative Modelling Approaches for Predicting Socio-Environmental Evolution in Highly Anthropized Coastal Lagoons. In Proceedings of the Conference on Information Technology for Social Good (Roma, Italy) (GoodIT '21). Association for Computing Machinery, New York, NY, USA, 204--209.

Digital Library

[3]

Wei Chen, Xiaoshen Xie, Jiale Wang, Biswajeet Pradhan, Haoyuan Hong, Dieu Tien Bui, Zhao Duan, and Jianquan Ma. 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151 (2017), 147--160.

[4]

Héctor M. Conesa and Francisco J. Jiménez-Cárceles. 2007. The Mar Menor lagoon (SE Spain): A singular natural ecosystem threatened by human activities. Marine Pollution Bulletin 54, 7 (2007), 839--849.

[5]

Giovanni Delnevo, Silvia Mirri, Catia Prandi, and Pietro Manzoni. 2023. An evaluation methodology to determine the actual limitations of a TinyML-based solution. Internet of Things 22 (2023), 100729.

[6]

S. García-Ayllón. 2017. Integrated management in coastal lagoons of highly complexity environments: Resilience comparative analysis for three case-studies. Ocean & Coastal Management 143 (2017), 16--25. The challenge of developing policies and management strategies under changing baselines and unbounded boundaries.

[7]

J. García-Pintado, M. Martínez-Mena, G.G. Barberá, J. Albaladejo, and V.M. Castillo. 2007. Anthropogenic nutrient sources and loads from a Mediterranean catchment into a coastal lagoon: Mar Menor, Spain. Science of The Total Environment 373, 1 (2007), 220--239.

[8]

Ikbal Gazalba, Nurul Gayatri Indah Reza, et al. 2017. Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. In 2017 2nd international conferences on information technology, information systems and electrical engineering (ICITISEE). IEEE, 294--298.

[9]

Noelia Guaita-García, Julia Martínez-Fernández, Carlos Javier Barrera-Causil, and H. Carl Fitz. 2022. Stakeholder analysis and prioritization of management measures for a sustainable development in the social-ecological system of the MarMenor (SE, Spain). Environmental Development 42 (2022), 100701.

[10]

Patricia Jimeno-Sáez, Javier Senent-Aparicio, José M. Cecilia, and Julio Pérez-Sánchez. 2020. Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental Research and Public Health 17, 4 (2020).

[11]

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017).

Digital Library

[12]

Ron Kohavi. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on Artificial intelligence-Volume 2. 1137--1143.

[13]

Alice Newton, Ana C. Brito, John D. Icely, Valérie Derolez, Inês Clara, Stewart Angus, Gerald Schernewski, Miguel Inácio, Ana I. Lillebø, Ana I. Sousa, Béchir Béjaoui, Cosimo Solidoro, Marko Tosic, Miguel Cañedo-Argüelles, Masumi Yamamuro, Sofia Reizopoulou, Hsiao-Chun Tseng, Donata Canu, Leonilde Roselli, Mohamed Maanan, Sónia Cristina, Ana Carolina Ruiz-Fernández, Ricardo F. de Lima, Björn Kjerfve, Nadia Rubio-Cisneros, Angel Pérez-Ruzafa, Concepción Marcos, Roberto Pastres, Fabio Pranovi, Maria Snoussi, Jane Turpie, Yurii Tuchkovenko, Brenda Dyack, Justin Brookes, Ramunas Povilanskas, and Valeriy Khokhlov. 2018. Assessing, quantifying and valuing the ecosystem services of coastal lagoons. Journal for Nature Conservation 44 (2018), 50--65.

[14]

Derek A Pisner and David M Schnyer. 2020. Support vector machine. In Machine learning. Elsevier, 101--121.

[15]

Philipp Probst, Marvin N Wright, and Anne-Laure Boulesteix. 2019. Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: data mining and knowledge discovery 9, 3 (2019), e1301.

[16]

Hassan Ramchoun, Youssef Ghanou, Mohamed Ettaouil, and Mohammed Amine Janati Idrissi. 2016. Multilayer perceptron: Architecture optimization and training. (2016).

[17]

Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim, and Asoke K Nandi. 2018. Credit card fraud detection using AdaBoost and majority voting. IEEE access 6 (2018), 14277--14284.

[18]

Jonas Ranstam and JA Cook. 2018. LASSO regression. Journal of British Surgery 105, 10 (2018), 1348--1348.

[19]

A. Razinkovas, Z. Gasiūnaité, P. Viaroli, and J. M. Zaldívar. 2008. Preface: European lagoons---need for further comparison across spatial and temporal scales. Hydrobiologia 611, 1 (01 Oct 2008), 1--4.

[20]

Javier Senent-Aparicio, Adrián López-Ballesteros, Anders Nielsen, and Dennis Trolle. 2021. A holistic approach for determining the hydrology of the mar menor coastal lagoon by combining hydrological & hydrodynamic models. Journal of Hydrology 603 (2021), 127150.

[21]

Aakanksha Sharaff and Harshil Gupta. 2019. Extra-tree classifier with metaheuristics approach for email classification. In Advances in Computer Communication and Computational Sciences: Proceedings of IC4S 2018. Springer, 189--197.

[22]

Xiaogang Su, Xin Yan, and Chih-Ling Tsai. 2012. Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics 4, 3 (2012), 275--294.

Digital Library

[23]

Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, and Guo-Sen Xie. 2017. Discriminative elastic-net regularized linear regression. IEEE Transactions on Image Processing 26, 3 (2017), 1466--1481.

Digital Library

Cited By

Delnevo GTumedei GGhini VPrandi C(2024)Toward a Digital Twin: Combining Sensing, Machine Learning, and Data Visualization for the Effective Management of a Coastal Lagoon Environment2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)10.1109/CCNC51664.2024.10454647(1-6)Online publication date: 6-Jan-2024
https://doi.org/10.1109/CCNC51664.2024.10454647

Index Terms

Evaluating the use of machine learning algorithms in environmental sensing for energy saving
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems
    2. Sensor networks
2. Computing methodologies
  1. Machine learning

Recommendations

Demonstration abstract: participatory sensing enabled environmental monitoring in smart cities
IPSN '14: Proceedings of the 13th international symposium on Information processing in sensor networks

This work describes a system demonstrator for environmental monitoring based on participatory sensing. Mobile environmental sensors carried by citizens are used to measure pollutant concentrations and provide this data to a Unified Sensing Platform. It ...
Prognostic of Soil Nutrients and Soil Fertility Index Using Machine Learning Classifier Techniques

Soil testing is a unique tool for finding the available soil reaction (pH), organic carbon, and nutrients status of the soil. It helps to select the suitable crops concerning available pH and soil nutrients level to increase crop production. In this ...
Using machine learning algorithms for housing price prediction

Housing price valuation is one of most important trading decisions.This study uses machine learning to develop housing price prediction models.This study analyzes the housing data of 5359 townhouses in Fairfax County, VA.The 10-fold cross-validation was ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

NET4us '23: Proceedings of the 2nd Workshop on Networked Sensing Systems for a Sustainable Society

October 2023

37 pages

ISBN:9798400703652

DOI:10.1145/3615991

Copyright © 2023 Owner/Author(s).

This work is licensed under a Creative Commons Attribution-NonCommercial International 4.0 License.

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 October 2023

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

European Union?s Horizon 2020 research and innovation programme

Conference

NET4us '23

Sponsor:

SIGMOBILE

NET4us '23: 2nd Workshop on Networked Sensing Systems for a Sustainable Society

October 6, 2023

Madrid, Spain

Acceptance Rates

NET4us '23 Paper Acceptance Rate 5 of 8 submissions, 63%;

Overall Acceptance Rate 5 of 8 submissions, 63%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
148
Total Downloads

Downloads (Last 12 months)148
Downloads (Last 6 weeks)22

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Delnevo GTumedei GGhini VPrandi C(2024)Toward a Digital Twin: Combining Sensing, Machine Learning, and Data Visualization for the Effective Management of a Coastal Lagoon Environment2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)10.1109/CCNC51664.2024.10454647(1-6)Online publication date: 6-Jan-2024
https://doi.org/10.1109/CCNC51664.2024.10454647

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents