scholarly journals Water Price Prediction for Increasing Market Efficiency Using Random Forest Regression: A Case Study in the Western United States

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 228 ◽  
Author(s):  
Ziyao Xu ◽  
Jijian Lian ◽  
Lingling Bin ◽  
Kaixun Hua ◽  
Kui Xu ◽  
...  
Keyword(s):  
United States ◽  
Random Forest ◽  
Model Performance ◽  
Western United States ◽  
Water Price ◽  
Random Forest Regression ◽  
Market Participants ◽  
Price Prediction ◽  
Variable Elimination

The existence of water markets establishes water prices, promoting trading of water from low- to high-valued uses. However, market participants can face uncertainty when asking and offering prices because water rights are heterogeneous, resulting in inefficiency of the market. This paper proposes three random forest regression models (RFR) to predict water price in the western United States: a full variable set model and two reduced ones with optimal numbers of variables using a backward variable elimination (BVE) approach. Transactions of 12 semiarid states, from 1987 to 2009, and a dataset containing various predictors, were assembled. Multiple replications of k-fold cross-validation were applied to assess the model performance and their generalizability was tested on unused data. The importance of price influencing factors was then analyzed based on two plausible variable importance rankings. Results show that the RFR models have good predictive power for water price. They outperform a baseline model without leading to overfitting. Also, the higher degree of accuracy of the reduced models is insignificant, reflecting the robustness of RFR to including lower informative variables. This study suggests that, due to its ability to automatically learn from and make predictions on data, RFR-based models can aid water market participants in making more efficient decisions.

Fragility curves for toppling of railroad locomotives

2020 ◽  
Vol 36 (4) ◽  
pp. 1623-1644
Author(s):  
Bruce F Maison
Keyword(s):  
United States ◽  
Size Effect ◽  
Case Studies ◽  
Fragility Curves ◽  
Western United States ◽  
Passenger Cars ◽  
Strong Earthquakes ◽  
Ground Shaking ◽  
Seismic Regions

Three locomotives that overturned (toppled) during strong earthquakes (>6.5M) are used as computer analytical case studies. The locomotives were at rest or traveling very slowly at the time of the earthquakes. Fragility curves are presented relating ground shaking intensity to likelihood of toppling. Supplemental studies determine the influence of various parameters, including track gauge, damping, sway-roll period, and size effect. The shaking intensities necessary for standard gauge (56.5 in) locomotives to topple are much greater than the median intensities of 2475-year earthquakes representative of those in high seismic regions of the western United States. A general conclusion is that standard gauge locomotives at rest are not susceptible to toppling in such earthquakes (≪50% chance). This can be expected to be the case as well for freight and passenger cars having sizes and slenderness similar to the case study locomotives. The study also provides insights about the toppling fragility of other large unanchored objects having similar proportions.

Water and Food in the American West

2018 ◽  
pp. 320-343
Author(s):  
Josué Medellín-Azuara ◽  
Jay Lund ◽  
Daniel A. Sumner
Keyword(s):  
United States ◽  
Global Economy ◽  
American West ◽  
Short Review ◽  
The United States ◽  
Western United States ◽  
Water Shortages ◽  
Groundwater Overdraft ◽  
Wide Range

The American West, the last region in the continental United States to be developed for extensive agriculture, is characterized by a wide range of biomes including arid, and semiarid regions, forest, and coastline. In its less water-rich places, this has forced the development of water supply infrastructure for agriculture and cities. The American West rapidly became an agricultural powerhouse to the United States and a major exporter of agricultural commodities in global economy. This chapter reviews agriculture in the western United States, followed by a short review of major western water issues for agriculture, including surface water shortages from drought and persistent groundwater overdraft. The California 2012–2016 drought is used as a case study to identify lessons for future food and fiber production in California, the western United States, and globally.

scholarly journals Spatially Explicit Mapping of Historical Population Density with Random Forest Regression: A Case Study of Gansu Province, China, in 1820 and 2000

2020 ◽  
Vol 12 (3) ◽  
pp. 1231 ◽  
Author(s):  
Fahao Wang ◽  
Weidong Lu ◽  
Jingyun Zheng ◽  
Shicheng Li ◽  
Xuezhen Zhang
Keyword(s):  
Spatial Distribution ◽  
Random Forest ◽  
Population Density ◽  
Census Data ◽  
Gansu Province ◽  
Spatially Explicit ◽  
Random Forest Regression ◽  
Independent Variables ◽  
Population Weighting

This study established a random forest regression model (RFRM) using terrain factors, climatic and river factors, distances to the capitals of provinces, prefectures (Fu, in Chinese Pinyin), and counties as independent variables to predict the population density. Then, using the RFRM, we explicitly reconstructed the spatial distribution of the population density of Gansu Province, China, in 1820 and 2000, at a resolution of 10 by 10 km. By comparing the explicit reconstruction with census data at the township level from 2000, we found that the RFRM-based approach mostly reproduced the spatial variability in the population density, with a determination coefficient (R2) of 0.82, a positive reduction of error (RE, 0.72) and a coefficient of efficiency (CE) of 0.65. The RFRM-based reconstructions show that the population of Gansu Province in 1820 was mostly distributed in the Lanzhou, Gongchang, Pingliang, Qinzhou, Qingyang, and Ningxia prefecture. The macro-spatial pattern of the population density in 2000 kept approximately similar with that in 1820. However, fine differences could be found. The 79.92% of the population growth of Gansu Province from 1820 to 2000 occurred in areas lower than 2500 m. As a result, the population weighting in the areas above 2500 m was ~9% in 1820 while it was greater than 14% in 2000. Moreover, in comparison to 1820, the population density intensified in Lanzhou, Xining, Yinchuan, Baiyin, Linxia, and Tianshui, while it weakened in Gongchang, Qingyang, Ganzhou, and Suzhou.

scholarly journals Evaluation of snow data assimilation using the ensemble Kalman filter for seasonal streamflow prediction in the western United States

2017 ◽  
Vol 21 (1) ◽  
pp. 635-650 ◽  
Author(s):  
Chengcheng Huang ◽  
Andrew J. Newman ◽  
Martyn P. Clark ◽  
Andrew W. Wood ◽  
Xiaogu Zheng
Keyword(s):  
United States ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Model Performance ◽  
Western United States ◽  
Streamflow Prediction ◽  
Model State ◽  
State Uncertainty ◽  
Seasonal Streamflow

Abstract. In this study, we examine the potential of snow water equivalent data assimilation (DA) using the ensemble Kalman filter (EnKF) to improve seasonal streamflow predictions. There are several goals of this study. First, we aim to examine some empirical aspects of the EnKF, namely the observational uncertainty estimates and the observation transformation operator. Second, we use a newly created ensemble forcing dataset to develop ensemble model states that provide an estimate of model state uncertainty. Third, we examine the impact of varying the observation and model state uncertainty on forecast skill. We use basins from the Pacific Northwest, Rocky Mountains, and California in the western United States with the coupled Snow-17 and Sacramento Soil Moisture Accounting (SAC-SMA) models. We find that most EnKF implementation variations result in improved streamflow prediction, but the methodological choices in the examined components impact predictive performance in a non-uniform way across the basins. Finally, basins with relatively higher calibrated model performance (> 0.80 NSE) without DA generally have lesser improvement with DA, while basins with poorer historical model performance show greater improvements.

scholarly journals A Brief Analysis of Key Machine Learning Methods for Predicting Medicare Payments Related to Physical Therapy Practices in the United States

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 57
Author(s):  
Shrirang A. Kulkarni ◽  
Jodh S. Pannu ◽  
Andriy V. Koval ◽  
Gabriel J. Merrin ◽  
Varadraj P. Gurupur ◽  
...  
Keyword(s):  
United States ◽  
Machine Learning ◽  
Physical Therapy ◽  
Random Forest ◽  
Generalized Additive Model ◽  
Additive Model ◽  
The United States ◽  
Random Forest Regression ◽  
Key Variables ◽  
Machine Learning Models

Background and objectives: Machine learning approaches using random forest have been effectively used to provide decision support in health and medical informatics. This is especially true when predicting variables associated with Medicare reimbursements. However, more work is needed to analyze and predict data associated with reimbursements through Medicare and Medicaid services for physical therapy practices in the United States. The key objective of this study is to analyze different machine learning models to predict key variables associated with Medicare standardized payments for physical therapy practices in the United States. Materials and Methods: This study employs five methods, namely, multiple linear regression, decision tree regression, random forest regression, K-nearest neighbors, and linear generalized additive model, (GAM) to predict key variables associated with Medicare payments for physical therapy practices in the United States. Results: The study described in this article adds to the body of knowledge on the effective use of random forest regression and linear generalized additive model in predicting Medicare Standardized payment. It turns out that random forest regression may have any edge over other methods employed for this purpose. Conclusions: The study provides a useful insight into comparing the performance of the aforementioned methods, while identifying a few intricate details associated with predicting Medicare costs while also ascertaining that linear generalized additive model and random forest regression as the most suitable machine learning models for predicting key variables associated with standardized Medicare payments.

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