The University of Southampton
University of Southampton Institutional Repository

Enabling proactive agricultural drainage reuse for improved water quality through collaborative networks and low-complexity data-driven modelling

Enabling proactive agricultural drainage reuse for improved water quality through collaborative networks and low-complexity data-driven modelling
Enabling proactive agricultural drainage reuse for improved water quality through collaborative networks and low-complexity data-driven modelling
With increasing prevalence of Wireless Sensor Networks (WSNs) in agriculture and hydrology, there exists an opportunity for providing a technologically viable solution for the conservation of already scarce fresh water resources. In this thesis, a novel framework is proposed for enabling a proactive management of agricultural drainage and nutrient losses at farm scale where complex models are replaced by in-situ sensing, communication and low complexity predictive models suited to an autonomous operation. This is achieved through the development of the proposed Water Quality Management using Collaborative Monitoring (WQMCM) framework that combines local farm-scale WSNs through an information sharing mechanism.

Under the proposed WQMCM framework, various functional modules are developed to demonstrate the overall mechanism: (1) neighbour learning and linking, (2) low-complexity predictive models for drainage dynamics, (3) low-complexity predictive model for nitrate losses, and (4) decision support model for drainage and nitrate reusability. The predictive models for drainage dynamics and nitrate losses are developed by abstracting model complexity from the traditional models (National Resource Conservation Method (NRCS) and De-Nitrification-DeComposition (DNDC) model respectively). Machine learning algorithms such as M5 decision tree, multiple linear regression, artificial neural networks, C4.5, and Naïve Bayes are used in this thesis. For the predictive models, validation is performed using 12-month long event dataset from a sub-catchment in Ireland.

Overall, the following contributions are achieved: (1) framework architecture and implementation for WQMCM for a networked catchment, (2) model development for low-complexity drainage discharge dynamics and nitrate losses by reducing number of model parameters to less than 50%, (3) validation of the predictive models for drainage and nitrate losses using M5 tree algorithm and measured catchment data. Additionally modelling results are compared with existing models and further tested with using other learning algorithms, and (4) development of a decision support model, based on Naïve Bayes algorithm, for suggesting reusability of drainage and nitrate losses.
Zia, Huma
74118b4c-35ab-44e8-a44f-daa4cc6f83e8
Zia, Huma
74118b4c-35ab-44e8-a44f-daa4cc6f83e8
Harris, Nicholas
237cfdbd-86e4-4025-869c-c85136f14dfd

Zia, Huma (2015) Enabling proactive agricultural drainage reuse for improved water quality through collaborative networks and low-complexity data-driven modelling. University of Southampton, Physical Sciences and Engineering, Doctoral Thesis, 168pp.

Record type: Thesis (Doctoral)

Abstract

With increasing prevalence of Wireless Sensor Networks (WSNs) in agriculture and hydrology, there exists an opportunity for providing a technologically viable solution for the conservation of already scarce fresh water resources. In this thesis, a novel framework is proposed for enabling a proactive management of agricultural drainage and nutrient losses at farm scale where complex models are replaced by in-situ sensing, communication and low complexity predictive models suited to an autonomous operation. This is achieved through the development of the proposed Water Quality Management using Collaborative Monitoring (WQMCM) framework that combines local farm-scale WSNs through an information sharing mechanism.

Under the proposed WQMCM framework, various functional modules are developed to demonstrate the overall mechanism: (1) neighbour learning and linking, (2) low-complexity predictive models for drainage dynamics, (3) low-complexity predictive model for nitrate losses, and (4) decision support model for drainage and nitrate reusability. The predictive models for drainage dynamics and nitrate losses are developed by abstracting model complexity from the traditional models (National Resource Conservation Method (NRCS) and De-Nitrification-DeComposition (DNDC) model respectively). Machine learning algorithms such as M5 decision tree, multiple linear regression, artificial neural networks, C4.5, and Naïve Bayes are used in this thesis. For the predictive models, validation is performed using 12-month long event dataset from a sub-catchment in Ireland.

Overall, the following contributions are achieved: (1) framework architecture and implementation for WQMCM for a networked catchment, (2) model development for low-complexity drainage discharge dynamics and nitrate losses by reducing number of model parameters to less than 50%, (3) validation of the predictive models for drainage and nitrate losses using M5 tree algorithm and measured catchment data. Additionally modelling results are compared with existing models and further tested with using other learning algorithms, and (4) development of a decision support model, based on Naïve Bayes algorithm, for suggesting reusability of drainage and nitrate losses.

PDF
__soton.ac.uk_ude_personalfiles_users_jo1d13_mydesktop_HZia - Final Thesis (Revised).pdf - Other
Download (3MB)

More information

Published date: June 2015
Organisations: University of Southampton, EEE

Identifiers

Local EPrints ID: 384511
URI: http://eprints.soton.ac.uk/id/eprint/384511
PURE UUID: bbe6f88c-53f8-437d-b2bd-254f7f57d902
ORCID for Nicholas Harris: ORCID iD orcid.org/0000-0003-4122-2219

Catalogue record

Date deposited: 22 Dec 2015 14:57
Last modified: 06 Jun 2018 13:08

Export record

Contributors

Author: Huma Zia
Thesis advisor: Nicholas Harris ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×