A support vector machine learning prediction model of evapotranspiration using real-time sensor node data

Waqas A. K. Afridi; S. C. Mukhopadhyay; Bandita Mainali

doi:10.5162/eurosensorsxxxvi/pt1.1

A support vector machine learning prediction model of evapotranspiration using real-time sensor node data

Waqas A. K. Afridi
, S. C. Mukhopadhyay
, Bandita Mainali

Macquarie University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

Abstract

An IoT-enabled smart sensor node has been developed to acquire real-time field data and formulate an adaptable prediction model to predict crop Evapotranspiration (ETc) using a Support Vector Machine (SVM) learning algorithm. Integrating the SVM algorithm with real-time sensor nodes offers great potential to improve spatial and temporal resolution of water data uncertainty. In the model development, key input features are measured in real-time and computed using mathematical equations such as Penman-Monteith, which include soil-environmental parameters.

Original language	English
Title of host publication	Proceedings of the Eurosensors XXXVI Conference, 1-4 September 2024, Debrecen, Hungary
Place of Publication	Germany
Publisher	Association for Sensors and Measurement
Pages	221-222
Number of pages	2
ISBN (Print)	9783910600034
DOIs	https://doi.org/10.5162/eurosensorsxxxvi/pt1.1
Publication status	Published - 2024
Externally published	Yes
Event	Eurosensors Conference - Debrecen, Hungary Duration: 1 Sept 2024 → 4 Sept 2024 Conference number: 36th

Conference

Conference	Eurosensors Conference
Country/Territory	Hungary
City	Debrecen
Period	1/09/24 → 4/09/24

Access to Document

10.5162/eurosensorsxxxvi/pt1.1

Cite this

@inproceedings{b7cc473588f24e84a7d6e2c440f9fb17,

title = "A support vector machine learning prediction model of evapotranspiration using real-time sensor node data",

abstract = "An IoT-enabled smart sensor node has been developed to acquire real-time field data and formulate an adaptable prediction model to predict crop Evapotranspiration (ETc) using a Support Vector Machine (SVM) learning algorithm. Integrating the SVM algorithm with real-time sensor nodes offers great potential to improve spatial and temporal resolution of water data uncertainty. In the model development, key input features are measured in real-time and computed using mathematical equations such as Penman-Monteith, which include soil-environmental parameters.",

author = "Afridi, \{Waqas A. K.\} and Mukhopadhyay, \{S. C.\} and Bandita Mainali",

year = "2024",

doi = "10.5162/eurosensorsxxxvi/pt1.1",

language = "English",

isbn = "9783910600034",

pages = "221--222",

booktitle = "Proceedings of the Eurosensors XXXVI Conference, 1-4 September 2024, Debrecen, Hungary",

publisher = "Association for Sensors and Measurement",

note = "Eurosensors Conference ; Conference date: 01-09-2024 Through 04-09-2024",

}

Afridi, WAK, Mukhopadhyay, SC & Mainali, B 2024, A support vector machine learning prediction model of evapotranspiration using real-time sensor node data. in Proceedings of the Eurosensors XXXVI Conference, 1-4 September 2024, Debrecen, Hungary. Association for Sensors and Measurement, Germany, pp. 221-222, Eurosensors Conference, Debrecen, Hungary, 1/09/24. https://doi.org/10.5162/eurosensorsxxxvi/pt1.1

A support vector machine learning prediction model of evapotranspiration using real-time sensor node data. / Afridi, Waqas A. K.; Mukhopadhyay, S. C.; Mainali, Bandita.
Proceedings of the Eurosensors XXXVI Conference, 1-4 September 2024, Debrecen, Hungary. Germany: Association for Sensors and Measurement, 2024. p. 221-222.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - A support vector machine learning prediction model of evapotranspiration using real-time sensor node data

AU - Afridi, Waqas A. K.

AU - Mukhopadhyay, S. C.

AU - Mainali, Bandita

N1 - Conference code: 36th

PY - 2024

Y1 - 2024

N2 - An IoT-enabled smart sensor node has been developed to acquire real-time field data and formulate an adaptable prediction model to predict crop Evapotranspiration (ETc) using a Support Vector Machine (SVM) learning algorithm. Integrating the SVM algorithm with real-time sensor nodes offers great potential to improve spatial and temporal resolution of water data uncertainty. In the model development, key input features are measured in real-time and computed using mathematical equations such as Penman-Monteith, which include soil-environmental parameters.

AB - An IoT-enabled smart sensor node has been developed to acquire real-time field data and formulate an adaptable prediction model to predict crop Evapotranspiration (ETc) using a Support Vector Machine (SVM) learning algorithm. Integrating the SVM algorithm with real-time sensor nodes offers great potential to improve spatial and temporal resolution of water data uncertainty. In the model development, key input features are measured in real-time and computed using mathematical equations such as Penman-Monteith, which include soil-environmental parameters.

U2 - 10.5162/eurosensorsxxxvi/pt1.1

DO - 10.5162/eurosensorsxxxvi/pt1.1

M3 - Conference Paper

SN - 9783910600034

SP - 221

EP - 222

BT - Proceedings of the Eurosensors XXXVI Conference, 1-4 September 2024, Debrecen, Hungary

PB - Association for Sensors and Measurement

CY - Germany

T2 - Eurosensors Conference

Y2 - 1 September 2024 through 4 September 2024

ER -

A support vector machine learning prediction model of evapotranspiration using real-time sensor node data

Abstract

Conference

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