Role of analytics within the energy, water and food nexus – An Alfalfa case study

Haile Woldesellasse, Rajesh Govindan, Tareq Al-Ansari

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

One of the biggest challenges today, is the need to feed a growing population. Considering climate change and the effect it may have on agriculture systems, the sustainable intensification of food production is a necessity. The objective of the research presented in this paper is to address the issues related to the large-scale stressors that can present a barrier to the current efforts for sustainable intensification of food production in Qatar. It relies on remote sensing imagery as the primary dataset owing to the ability of the satellite sensors to cheaply gather data with good areal coverage and spatial resolution, and with added benefit of repeated surveys from the satellite overpasses over a given area. The steps of the proposed methodology developed in this research include: (a) image processing for training and prediction using neural networks; (b) extraction of atmospheric and surface features that can directly affect greenhouse operations, e.g. those related to land and water resources, and energy requirements; (c) formulation of a mixed integer non-linear programming (MINLP) framework using surrogate modelling for the maximization of the crop productivity objective; (d) development of GIS database augmented with recommender systems. The results obtained so far are promising, with root mean squared percentage error of 11.93 for the crop water demand prediction, and it is envisaged that the proposed development of a smart geospatial framework with predictive capabilities, EWF optimization, and augmented GIS will facilitate for informed policy development aimed at the success of the food security programs in Qatar.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
PublisherElsevier B.V.
Pages997-1002
Number of pages6
DOIs
Publication statusPublished - 1 Jan 2018

Publication series

NameComputer Aided Chemical Engineering
Volume44
ISSN (Print)1570-7946

Fingerprint

Geographic information systems
Crops
Water
Overpasses
Satellites
Recommender systems
Greenhouses
Nonlinear programming
Water resources
Climate change
Agriculture
Remote sensing
Image processing
Productivity
Neural networks
Sensors
Nexus

Keywords

  • EWF nexus
  • food security
  • MINLP
  • neural network
  • Optimization

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

Woldesellasse, H., Govindan, R., & Al-Ansari, T. (2018). Role of analytics within the energy, water and food nexus – An Alfalfa case study. In Computer Aided Chemical Engineering (pp. 997-1002). (Computer Aided Chemical Engineering; Vol. 44). Elsevier B.V.. https://doi.org/10.1016/B978-0-444-64241-7.50161-0

Role of analytics within the energy, water and food nexus – An Alfalfa case study. / Woldesellasse, Haile; Govindan, Rajesh; Al-Ansari, Tareq.

Computer Aided Chemical Engineering. Elsevier B.V., 2018. p. 997-1002 (Computer Aided Chemical Engineering; Vol. 44).

Research output: Chapter in Book/Report/Conference proceedingChapter

Woldesellasse, H, Govindan, R & Al-Ansari, T 2018, Role of analytics within the energy, water and food nexus – An Alfalfa case study. in Computer Aided Chemical Engineering. Computer Aided Chemical Engineering, vol. 44, Elsevier B.V., pp. 997-1002. https://doi.org/10.1016/B978-0-444-64241-7.50161-0
Woldesellasse H, Govindan R, Al-Ansari T. Role of analytics within the energy, water and food nexus – An Alfalfa case study. In Computer Aided Chemical Engineering. Elsevier B.V. 2018. p. 997-1002. (Computer Aided Chemical Engineering). https://doi.org/10.1016/B978-0-444-64241-7.50161-0
Woldesellasse, Haile ; Govindan, Rajesh ; Al-Ansari, Tareq. / Role of analytics within the energy, water and food nexus – An Alfalfa case study. Computer Aided Chemical Engineering. Elsevier B.V., 2018. pp. 997-1002 (Computer Aided Chemical Engineering).
@inbook{65c7fb4bca254ea4a2b0ed3f90e96646,
title = "Role of analytics within the energy, water and food nexus – An Alfalfa case study",
abstract = "One of the biggest challenges today, is the need to feed a growing population. Considering climate change and the effect it may have on agriculture systems, the sustainable intensification of food production is a necessity. The objective of the research presented in this paper is to address the issues related to the large-scale stressors that can present a barrier to the current efforts for sustainable intensification of food production in Qatar. It relies on remote sensing imagery as the primary dataset owing to the ability of the satellite sensors to cheaply gather data with good areal coverage and spatial resolution, and with added benefit of repeated surveys from the satellite overpasses over a given area. The steps of the proposed methodology developed in this research include: (a) image processing for training and prediction using neural networks; (b) extraction of atmospheric and surface features that can directly affect greenhouse operations, e.g. those related to land and water resources, and energy requirements; (c) formulation of a mixed integer non-linear programming (MINLP) framework using surrogate modelling for the maximization of the crop productivity objective; (d) development of GIS database augmented with recommender systems. The results obtained so far are promising, with root mean squared percentage error of 11.93 for the crop water demand prediction, and it is envisaged that the proposed development of a smart geospatial framework with predictive capabilities, EWF optimization, and augmented GIS will facilitate for informed policy development aimed at the success of the food security programs in Qatar.",
keywords = "EWF nexus, food security, MINLP, neural network, Optimization",
author = "Haile Woldesellasse and Rajesh Govindan and Tareq Al-Ansari",
year = "2018",
month = "1",
day = "1",
doi = "10.1016/B978-0-444-64241-7.50161-0",
language = "English",
series = "Computer Aided Chemical Engineering",
publisher = "Elsevier B.V.",
pages = "997--1002",
booktitle = "Computer Aided Chemical Engineering",

}

TY - CHAP

T1 - Role of analytics within the energy, water and food nexus – An Alfalfa case study

AU - Woldesellasse, Haile

AU - Govindan, Rajesh

AU - Al-Ansari, Tareq

PY - 2018/1/1

Y1 - 2018/1/1

N2 - One of the biggest challenges today, is the need to feed a growing population. Considering climate change and the effect it may have on agriculture systems, the sustainable intensification of food production is a necessity. The objective of the research presented in this paper is to address the issues related to the large-scale stressors that can present a barrier to the current efforts for sustainable intensification of food production in Qatar. It relies on remote sensing imagery as the primary dataset owing to the ability of the satellite sensors to cheaply gather data with good areal coverage and spatial resolution, and with added benefit of repeated surveys from the satellite overpasses over a given area. The steps of the proposed methodology developed in this research include: (a) image processing for training and prediction using neural networks; (b) extraction of atmospheric and surface features that can directly affect greenhouse operations, e.g. those related to land and water resources, and energy requirements; (c) formulation of a mixed integer non-linear programming (MINLP) framework using surrogate modelling for the maximization of the crop productivity objective; (d) development of GIS database augmented with recommender systems. The results obtained so far are promising, with root mean squared percentage error of 11.93 for the crop water demand prediction, and it is envisaged that the proposed development of a smart geospatial framework with predictive capabilities, EWF optimization, and augmented GIS will facilitate for informed policy development aimed at the success of the food security programs in Qatar.

AB - One of the biggest challenges today, is the need to feed a growing population. Considering climate change and the effect it may have on agriculture systems, the sustainable intensification of food production is a necessity. The objective of the research presented in this paper is to address the issues related to the large-scale stressors that can present a barrier to the current efforts for sustainable intensification of food production in Qatar. It relies on remote sensing imagery as the primary dataset owing to the ability of the satellite sensors to cheaply gather data with good areal coverage and spatial resolution, and with added benefit of repeated surveys from the satellite overpasses over a given area. The steps of the proposed methodology developed in this research include: (a) image processing for training and prediction using neural networks; (b) extraction of atmospheric and surface features that can directly affect greenhouse operations, e.g. those related to land and water resources, and energy requirements; (c) formulation of a mixed integer non-linear programming (MINLP) framework using surrogate modelling for the maximization of the crop productivity objective; (d) development of GIS database augmented with recommender systems. The results obtained so far are promising, with root mean squared percentage error of 11.93 for the crop water demand prediction, and it is envisaged that the proposed development of a smart geospatial framework with predictive capabilities, EWF optimization, and augmented GIS will facilitate for informed policy development aimed at the success of the food security programs in Qatar.

KW - EWF nexus

KW - food security

KW - MINLP

KW - neural network

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=85050640132&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050640132&partnerID=8YFLogxK

U2 - 10.1016/B978-0-444-64241-7.50161-0

DO - 10.1016/B978-0-444-64241-7.50161-0

M3 - Chapter

T3 - Computer Aided Chemical Engineering

SP - 997

EP - 1002

BT - Computer Aided Chemical Engineering

PB - Elsevier B.V.

ER -