A Machine Learning Based Multi-model ensemble Approach to reconstruct the historical monthly precipitation over Oklahoma using NOAA's SPEAR dataset

Neupane, Dilip

A Machine Learning Based Multi-model ensemble Approach to reconstruct the historical monthly precipitation over Oklahoma using NOAA's SPEAR dataset

dc.contributor.advisor	Yang, Tiantian
dc.contributor.author	Neupane, Dilip
dc.contributor.committeeMember	Hong, Yang
dc.contributor.committeeMember	Kirstetter, Pierre Emmanuel
dc.date.accessioned	2024-05-17T17:06:39Z
dc.date.available	2024-05-17T17:06:39Z
dc.date.issued	2024-05-10
dc.date.manuscript	2024
dc.description.abstract	General Circulation Models (GCMs) are important tools in simulating and projecting future precipitation at the decadal scale. However, it is inevitable that simulation and projection errors and uncertainty exist in GCMs, hindering their applications for regional water resources planning. Different post-processing tools are available to address the uncertainty issues associated with GCMs and to utilize these tools better for regional water resources planning. For example, a multi-model ensemble (MME) could reduce uncertainties from different GCMs and help reduce the model biases from a single model. In this study, we employed multiple Machine Learning algorithms (MLs) to combine ensemble members from NOAA’s Seamless System for Prediction and EArth System Research (SPEAR) to reconstruct historical monthly precipitation over Oklahoma during a study period (1981-2014). The employed MLs include Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Classification And Regression Trees (CART). The performances of the employed MLs are benchmarked with Simple Model Averaging (SMA), Bayesian Model Averaging (BMA), and Reliability Ensemble Averaging (REA). Our result echoes previous studies where the raw precipitation simulation from SPEAR presents significant simulation bias and marginal simulation skills. Different spatial and seasonal patterns of the simulation bias and skill are also observed over our study region. All the employed multi-model averaging techniques have delivered better performances than any single ensemble member from SPEAR. The employed MLs have outperformed SMA, BMA, and REA, which is evident from the reduction of bias and skill improvement. In general, this study highlights future applications of other data-driven techniques in post-processing the multi-model simulation from GCMs.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/340367
dc.language	en_US	en_US
dc.subject	Precipitation	en_US
dc.subject	Multi-Model Ensemble	en_US
dc.subject	Global Climate Model (GCM)	en_US
dc.subject	Machine Learning	en_US
dc.subject	Model averaging	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	A Machine Learning Based Multi-model ensemble Approach to reconstruct the historical monthly precipitation over Oklahoma using NOAA's SPEAR dataset	en_US
ou.group	Gallogly College of Engineering::School of Civil Engineering and Environmental Science	en_US
shareok.orcid	0009-0003-2986-7986	en_US