DEVELOPMENT OF A DECISION SUPPORT SYSTEM WEBTOOL FOR HISTORIC AND FUTURE LOW FLOW ESTIMATION IN THE NORTHEAST UNITED STATES WITH APPLICATIONS OF MACHINE LEARNING FOR ADVANCING PHYSICAL AND STATISTICAL METHODOLOGIES

Droughts are a global challenge and anthropogenic climate change is expected to increase the frequency and severity of extreme low flow events. A major challenge for resource managers is how best to incorporate future climate change projections into low flow event estimations, especially in ungaged basins. Using both physically based hydrology models and statistical models, this dissertation contributes novel methodologies to three key challenges associated with 7-day, 10-year low flow (7Q10) estimation in the northeast United States. Chapter 2 builds upon statistically based 7Q10 estimation in ungaged basins by comparing multiple machine learning algorithms to classical statistical methodologies. This chapter’s objective is to identify a robust statistical methodology applicable for the entire northeast U.S. that includes statistically significant climate variables that allow for the incorporation of climate change. Results suggest that the random forest method can provide regional 7Q10 estimates with similar errors to current, state-by-state 7Q10 estimates. Chapter 3 tests the applicability of a novel machine learning algorithm, Fuzzy C-Means clustering, to calibrate rainfall-runoff models in ungaged basins for both daily streamflow and 7Q10 estimation. Future updates to national rainfall-runoff models, which can directly incorporate climate change projections into calculations, will allow these models to be created in ungaged basins, but they will require extensive calibration and/or verification. Results suggest that this methodology significantly improves daily streamflow estimation but fails to improve 7Q10 estimation. Chapter 4 summarizes the development of a stakeholder-driven decision support system (DSS) web-application for calculating the 7Q10 at gages and estimating the 7Q10 in ungaged basins with projected climate changes. By incorporating the statistical model from Chapter 2 into the DSS and comparing the results to the physical modeling from Chapter 3, the DSS can estimate the impact of future temperature and precipitation changes on 7Q10s. This work highlights advancements in physical and statistical modeling techniques for 7Q10 estimation in ungaged basins and assists resource managers in addressing a growing need for incorporating anticipated climate change into drought calculations

Low-Flow (7-Day, 10-Year) Classical Statistical and Improved Machine Learning Estimation Methodologies

Water resource managers require accurate estimates of the 7-day, 10-year low flow (7Q10) of streams for many reasons, including protecting aquatic species, designing wastewater treatment plants, and calculating municipal water availability. StreamStats, a publicly available web application developed by the United States Geologic Survey that is commonly used by resource managers for estimating the 7Q10 in states where it is available, utilizes state-by-state, locally calibrated regression equations for estimation. This paper expands StreamStats’ methodology and improves 7Q10 estimation by developing a more regionally applicable and generalized methodology for 7Q10 estimation. In addition to classical methodologies, namely multiple linear regression (MLR) and multiple linear regression in log space (LTLR), three promising machine learning algorithms, random forest (RF) decision trees, neural networks (NN), and generalized additive models (GAM), are tested to determine if more advanced statistical methods offer improved estimation. For illustrative purposes, this methodology is applied to and verified for the full range of unimpaired, gaged basins in both the northeast and mid-Atlantic hydrologic regions of the United States (with basin sizes ranging from 2–1419 mi2) using leave-one-out cross-validation (LOOCV). Pearson’s correlation coefficient (R2), root mean square error (RMSE), Kling–Gupta Efficiency (KGE), and Nash–Sutcliffe Efficiency (NSE) are used to evaluate the performance of each method. Results suggest that each method provides varying results based on basin size, with RF displaying the smallest average RMSE (5.85) across all ranges of basin sizes

Constrained snake vs. conventional snake for carotid ultrasound automated IMT measurements on multi-center data sets

Accurate intima-media thickness (IMT) measurement of the carotid artery from minimal plaque ultrasound images is a relevant clinical need, since IMT increase is related to the progression of atherosclerosis. In this paper, we describe a novel dual snake-based model for the high-performance carotid IMT measurement, called Carotid Measurement Using Dual Snakes (CMUDS). Snakes (which are deformable contours) adapt to the lumen-intima (LI) and media-adventitia (MA) interfaces, thus enabling the IMT computation as distance between the LI and MA snakes. However, traditional snakes might be unable to maintain a correct distance and in some spatial location along the artery, it might even collapse between them or diverge. The technical improvement of this work is the definition of a dual snake-based constrained system, which prevents the LI and MA snakes from collapsing or bleeding, thus optimizing the IMT estimation. The CMUDS system consists of two parametric models automatically initialized using the far adventitia border which we automatically traced by using a previously developed multi-resolution approach. The dual snakes evolve simultaneously and are constrained by the distances between them, ensuring the regularization of LI/MA topology. We benchmarked our automated CMUDS with the previous conventional semi-automated snake system called Carotid Measurement Using Single Snake (CMUSS). Two independent readers manually traced the LIMA boundaries of a multi-institutional, multi-ethnic, and multi-scanner database of 665 CCA longitudinal 2D images. We evaluated our system performance by comparing it with the gold standard as traced by clinical readers. CMUDS and CMUSS correctly processed 100% of the 665 images. Comparing the performance with respect to the two readers, our automatically measured IMT was on average very close to that of the two readers (IMT measurement biases for CMUSS was equal to −0.011 ± 0.329 mm and −0.045 ± 0.317 mm, respectively, while for CMUDS, it was 0.030 ± 0.284 mm and −0.004 ± 0.273 mm, respectively). The Figure-of-Merit of the system was 98.5% and 94.4% for CMUSS, while 96.0% and 99.6% for CMUDS, respectively. Results showed that the dual-snake system CMUDS reduced the IMT measurement error accuracy (Wilcoxon, p < 0.02) and the IMT error variability (Fisher, p < 3 × 10−2). We propose the CMUDS technique for use in large multi-centric studies, where the need for a standard, accurate, and automated IMT measurement technique is require

Inter- and intra-observer variability analysis of completely automated cIMT measurement software (AtheroEdge™) and its benchmarking against commercial ultrasound scanner and expert Readers

The purpose of this study was to evaluate the measurement error and inter- and intra-observer variability of completely off-line automated and semi-automated carotid intima-media thickness (cIMT) measurement software (AtheroEdge™).Two hundred carotid ultrasound images from 50 asymptomatic women were analyzed. AtheroEdge™ was benchmarked against a commercial system (Syngo, Siemens) using automated and semi-automated modes. The measurement error and inter- and intra-observer variability of AtheroEdge™ were tested using three readings.The measurement error of AtheroEdge™ compared to the commercial software was 0.002±0.019. mm (r=0.99) in the automated mode and -0.001±0.004. mm in the semi-automated mode (r=0.99). The measurement error of AtheroEdge™ compared to the mean value of the three expert Readers (cIMT bias) for the automated and semi-automated methods was -0.0004±0.158. mm and -0.008±0.157. mm, respectively. The Figure-of-Merit was 99.8% and 99.9% when compared to the commercial ultrasound scanner (using the automated and semi-automated method, respectively) and was 99.9% and 98.9% when compared to the mean value of the three expert Readers. Regarding inter- and intra-observer variability, the intra-class correlation coefficient of the three independent users using the semi-automated AtheroEdge™ was 0.98.AtheroEdge™ showed a measurement performance comparable to the commercial ultrasound scanner software and the expert Readers' tracings. AtheroEdge™ belongs to a class of automated systems that could find application in processing large datasets for common carotid arteries, avoiding subjectivity in cIMT measurement

Data_Sheet_1_Fuzzy C-Means clustering for physical model calibration and 7-day, 10-year low flow estimation in ungaged basins: comparisons to traditional, statistical estimates.docx

In the northeast U.S., resource managers commonly apply 7-day, 10-year (7Q10) low flow estimates for protecting aquatic species in streams. In this paper, the efficacy of process-based hydrologic models is evaluated for estimating 7Q10s compared to the United States Geological Survey's (USGS) widely applied web-application StreamStats, which uses traditional statistical regression equations for estimating extreme flows. To generate the process-based estimates, the USGS's National Hydrologic Modeling (NHM-PRMS) framework (which relies on traditional rainfall-runoff modeling) is applied with 36 years of forcings from the Daymet climate dataset to a representative sample of ninety-four unimpaired gages in the Northeast and Mid-Atlantic U.S. The rainfall-runoff models are calibrated to the measured streamflow at each gage using the recommended NHM-PRMS calibration procedure and evaluated using Kling-Gupta Efficiency (KGE) for daily streamflow estimation. To evaluate the 7Q10 estimates made by the rainfall-runoff models compared to StreamStats, a multitude of error metrics are applied, including median relative bias (cfs/cfs), Root Mean Square Error (RMSE) (cfs), Relative RMSE (RRMSE) (cfs/cfs), and Unit-Area RMSE (UA-RMSE) (cfs/mi2). The calibrated rainfall-runoff models display both improved daily streamflow estimation (median KGE improving from 0.30 to 0.52) and 7Q10 estimation (smaller median relative bias, RMSE, RRMSE, and UA-RMSE, especially for basins larger than 100 mi2). The success of calibration is extended to ungaged locations using the machine learning algorithm Fuzzy C-Means (FCM) clustering, finding that traditional K-Means clustering (FCM clustering with no fuzzification factor) is the preferred method for model regionalization based on (1) Silhouette Analysis, (2) daily streamflow KGE, and (3) 7Q10 error metrics. The optimal rainfall-runoff models created with clustering show improvement for daily streamflow estimation (a median KGE of 0.48, only slightly below that of the calibrated models at 0.52); however, these models display similar error metrics for 7Q10 estimation compared to the uncalibrated models, neither of which provide improved error compared to the statistical estimates. Results suggest that the rainfall-runoff models calibrated to measured streamflow data provide the best 7Q10 estimation in terms of all error metrics except median relative bias, but for all models applicable to ungaged locations, the statistical estimates from StreamStats display the lowest error metrics in every category.</p

Multiplicity dependence of light (anti-)nuclei production in p–Pb collisions at sNN=5.02 TeV

The measurement of the deuteron and anti-deuteron production in the rapidity range −1 < y < 0 as a function of transverse momentum and event multiplicity in p–Pb collisions at √sNN = 5.02 TeV is presented. (Anti-)deuterons are identified via their specific energy loss dE/dx and via their time-of- flight. Their production in p–Pb collisions is compared to pp and Pb–Pb collisions and is discussed within the context of thermal and coalescence models. The ratio of integrated yields of deuterons to protons (d/p) shows a significant increase as a function of the charged-particle multiplicity of the event starting from values similar to those observed in pp collisions at low multiplicities and approaching those observed in Pb–Pb collisions at high multiplicities. The mean transverse particle momenta are extracted from the deuteron spectra and the values are similar to those obtained for p and particles. Thus, deuteron spectra do not follow mass ordering. This behaviour is in contrast to the trend observed for non-composite particles in p–Pb collisions. In addition, the production of the rare 3He and 3He nuclei has been studied. The spectrum corresponding to all non-single diffractive p-Pb collisions is obtained in the rapidity window −1 < y < 0 and the pT-integrated yield dN/dy is extracted. It is found that the yields of protons, deuterons, and 3He, normalised by the spin degeneracy factor, follow an exponential decrease with mass number