Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan
PhD Thesis
Title | Probabilistic and artificial intelligence modelling of |
---|---|
Type | PhD Thesis |
Authors | |
Author | Ali, Mumtaz |
Supervisor | Deo, Ravinesh C. |
Downs, Nathan J. | |
Maraseni, Tek | |
Institution of Origin | University of Southern Queensland |
Qualification Name | Doctor of Philosophy |
Number of Pages | 211 |
Year | 2019 |
Digital Object Identifier (DOI) | https://doi.org/10.26192/S847-M467 |
Abstract | Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations. |
Keywords | machine lerning, mathematics, environmental modelling, agricultural crops |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
460207. Modelling and simulation | |
370202. Climatology | |
300202. Agricultural land management | |
490199. Applied mathematics not elsewhere classified | |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
https://research.usq.edu.au/item/q5xy1/probabilistic-and-artificial-intelligence-modelling-of-drought-and-agricultural-crop-yield-in-pakistan
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