ScholarWorks

ScholarWorks is an open access repository for the capture of the intellectual work of Montana State University (MSU) in support of its teaching, research and service missions. MSU ScholarWorks is a central point of discovery for accessing, collecting, sharing, preserving, and distributing knowledge to the Montana State University community and the world.

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Scholarship & Research

Recent Submissions

The Role of Attentional Control in Deception While Under Cognitive Load
(Wiley, 2026-04)
Brennan, Evan
;
Hutchison, Keith A.
We examined the association between attentional control and the ability to lie. After being tested for individual differences in attentional control, pairs of participants answered autobiographical questions truthfully and dishonestly while under high and low cognitive load, with one participant answering questions and the other participant detecting their answers for veracity. Lying ability was assessed by participants' reaction times and their ability to evade detection. We hypothesized that RTs would be associated with detection accuracy, that low relative to high attentional control would be associated with longer lie RTs, and that high attentional control would be associated with a better ability to evade detection when lying under high cognitive load. Results demonstrated that RTs were indeed associated with detection accuracy and that attentional control is associated with RTs when lying, but not with deception detection. Cognitive load was not associated with any of the variables. We discuss many future research directions.
Integrating Satellite Imagery and Infield Sensors for Daily Spatial Plant Evapotranspiration Prediction: A Machine Learning-Driven Approach
(IEEE, 2024-05)
Nazrul Shimim, Farshina
;
Glenn, Ethan M.
;
Felegari, Shilan
;
Griesbaum, Brett
;
Fike, John
Continuous monitoring of crop health, especially plant EvapoTranspiration (ET) helps make efficient irrigation decisions. While various infield and remote sensing technologies provide valuable data, challenges lie in obtaining fine-resolution spatiotemporal observations. Moreover, existing research primarily addresses gap-filling past data, limiting the potential for advanced irrigation practice optimization tethered to historical observation-based management. Our work enables preemptive irrigation management by predicting the spatial ET on a day-ahead basis. We implement a Machine Learning (ML)-driven method, named Data Fusion Using Satellite and Infield Observations for Neural-network-based prediction of spatiotemporal plant EvapoTranspiration (DFUSIONET). This methodology in-cludes an Earth Observation (EO) based spatial ET-generation model, a unique interpolation method named Proportional-offset Interpolation (POI) for temporal gap-filling, and a multi-input Feedforward Neural Network (FNN) for predicting daily spatial ET. Results demonstrate that the POI method outperforms other interpolation techniques with an RMSE of 0.11 mm d-1, and the FNN exhibits a RMSE range of [0.13, 0.31] mm d-1.DFUSIONET integrates satellite imagery, infield sensor data, and meteoro-logical parameters and captures spatiotemporal variations of site-and crop-specific parameters to predict plant ET. This forecasting strategy is beneficial for efficient preemptive irrigation management, contributing to sustainable agricultural practices and improved resource utilization in precision agriculture.
A Reinforcement Learning Approach to the Dynamic Job Scheduling Problem
(IEEE, 2022-11)
Nazrul Shimim, Farshina
;
Whitaker, Bradley M.
Scheduling or day-ahead planning improves the efficiency of a process and often leads to other advantages such as energy savings and increased revenue. However, most real-world scheduling problems are very complicated and are usually affected by several external parameters. Hence, finding the best schedule given a set of jobs requires extensive calculations that increase exponentially with the number of jobs. Traditional schedulers are, at times, unable to address uncertainties in the system. This paper proposes a Reinforcement Learning approach for solving the Job Scheduling Problem in a dynamic environment with an aim to minimize the peak instantaneous electricity consumption. The training instance is randomly reset after a certain period and the solver uses online training to adapt to the new environment. Simulation results show that both the proposed approach and a Genetic Algorithm-based approach achieve the minimum peak power consumption possible, which is 58% less than on-demand dispatch. Also, for 82.2% of the simulations, our method finds a better schedule than its initialization.
AURANET: Recurrent Alternate Update Training for Spatiotemporal Prediction with Incomplete Observations
(IEEE, 2025-05)
Nazrul Shimim, Farshina
;
Felegari, Shilan
;
Griesbaum, Brett
;
Whitaker, Bradley M.
;
Nugent, Paul W.
Spatiotemporal prediction using data fusion from multiple sources presents significant challenges, especially without proper equipment or resources causing incomplete observations or missing data in the prediction hyperspace. Statistical interpolation methods and conventional machine learning models struggle to capture the true data distribution in highly sparse spatiotemporal settings. To address these limitations, we propose AURANET (Alternate Update with Recurrent Approach for Neural nETworks)—a novel training framework that alternately updates the model trainable parameters and the corresponding predictions iteratively until a convergence criterion is met for the available observations. AURANET operates in two major steps—(1) with sparse observations as input, it generates a recurrent chain-of-prediction to gap-fill a target spatiotemporal data cube with fixed network parameters (i.e., weights and biases), and (2) at the end of the prediction chain, it updates these network parameters only for the available ground truth observations, i.e., use a subset of the predictions to train the neural network at each training step. In this paper, AURANET is implemented to gapfill and predict daily spatial plant evapotranspiration (ET) data in Montana potato fields, where complete spatial observations are available only on certain days of the growing season, and complete temporal observations are available only for one specific location in the field. The corresponding predictions show a validation MSE of 1.36 mm2 d-2 on the 2023 growing season and a test error of -1.47 mm d-1 on the 2024 growing season. The AURANET framework extends beyond ET prediction and can be applied to various domains requiring recurrent learning structure with incomplete data.
High-resolution surface and rootzone soil moisture over US cropland: A novel framework assimilating multi-source remote sensing data, machine learning, and the Layered Green and Ampt Infiltration with Redistribution model
(Elsevier BV, 2025-12)
Cai, Shuohao
;
Xu, Yijia
;
Yang, Zhengwei
;
Crow, Wade T.
;
Zhang, Zhou
Accurate and high spatiotemporal resolution soil moisture (SM) monitoring in cropland is important for water resource management, drought forecasting, and nutrient transport estimation at the field scale for sustainable crop production. Although recent research has applied machine learning (ML) to downscale coarse-resolution satellite SM products, most of this past work has focused only on surface SM estimation, and the performance of rootzone SM products has not been intensively evaluated in cropland. This study introduces a novel framework that integrates multi-source satellite-based ML models with the Layered Green and Ampt Infiltration with Redistribution (LGAR) model to produce high-resolution (100 m, hourly) SM products for both the surface layer (0–5 cm) and rootzone (0–100 cm) across cropland in the contiguous United States (CONUS). First, six ML models were trained using multiple high-resolution remote sensing datasets (Sentinel-1, Sentinel-2, and Landsat) to predict surface and rootzone SM. These ML predictions were then assimilated into the LGAR model using the ensemble Kalman filter (EnKF). The framework was developed and validated using an eight-fold cross-validation scheme with in-situ data from 431 cropland sites across CONUS, sourced from three networks (SCAN, USCRN, and PSA). The 100-m hourly SM data from this framework surpasses existing products (9-km SMAP L4, SMAP-based 1-km thermal hydraulic disaggregation of SM product) in spatial and temporal resolution and captures rootzone SM that is not available in the SMAP-HydroBlocks SM product. It achieves good performance, with median bias-corrected root mean squared error (ubRMSE) of 0.053 m3/m3 and median Kling-Gupta efficiency (KGE) of 0.379 in the surface layer, and median ubRMSE of 0.027 m3/m3 and median KGE of 0.302 in the rootzone. While the framework demonstrates strong performance, its accuracy varies across climatic regimes, with surface SM performing better in non-humid areas (median KGE = 0.375 versus median KGE = 0.416) and rootzone SM in humid regions (median KGE = 0.313 versus median KGE = 0.127). This high-resolution cropland SM product can potentially benefit multiple agricultural applications, such as irrigation management and nutrient leaching estimation, and provide valuable insights to support farmers and land managers in decision-making processes.