scholarly journals HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1752
Author(s):  
Jacob L. Conrad ◽  
Michael D. Morphew ◽  
Rex L. Baum ◽  
Benjamin B. Mirus
Keyword(s):  
Time Series Data ◽  
Water Pressure ◽  
Shallow Landslides ◽  
Series Data ◽  
False Alarms ◽  
Warning Systems ◽  
Operating Characteristics ◽  
Hydrologic Time Series ◽  
Area Of Interest ◽  
The U.S

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.

The Determinants Of Personal Saving In The U.S.

2010 ◽  
Vol 26 (5) ◽  
Author(s):  
Myeong Hwan Kim
Keyword(s):  
Economic Performance ◽  
Income Tax ◽  
Time Series Data ◽  
Series Data ◽  
Personal Income Tax ◽  
Font Size ◽  
Personal Saving ◽  
External Variables ◽  
Real Estate Loan ◽  
The U.S

<p class="MsoNormal" style="text-justify: inter-ideograph; text-align: justify; margin: 0in 36.1pt 0pt 0.5in; mso-pagination: none;"><span style="color: black; font-size: 10pt;"><span style="font-family: Times New Roman;">In this study, a number of internal and external variables that could affect personal saving are examined using regression to show how they are related to personal saving. The empirical study is performed using the time series data of the U.S. between the years 1950 and 2007. The findings reveal that personal saving is highly dependent on personal income, tax, credit outstanding and status of employment, while dependency ratio, current real estate loan, real interest rate and status of economic performance are indeterminate.<em><span style="mso-bidi-font-style: normal;"></span></em></span></span></p>

scholarly journals MEASURING POVERTY CYCLES IN THE U.S. 1959–2013

2018 ◽  
Vol 24 (4) ◽  
pp. 1737-1754 ◽  
Author(s):  
Marinko Škare ◽  
Romina Pržiklas Družeta ◽  
Damian Škare
Keyword(s):  
Business Cycles ◽  
Time Series Data ◽  
Well Being ◽  
Poverty Rate ◽  
Series Data ◽  
Poverty Dynamics ◽  
The Usa ◽  
Shed Light ◽  
The U.S ◽  
Economic Well Being

This paper aims to shed light on the nature of poverty as a dynamic process by examining poverty cycles, their magnitudes, and their asymmetry. The designated benchmark country is the USA due to the availability of time series data making comprehensive analyses possible. We use Harding and Pagan (2002) and the Cardinale and Taylor (2009) model to isolate poverty cycles in the U.S. during 1959–2013. Once isolated, we test the poverty cycles for duration dependency, and their synchronization with the U.S. business cycles observed over the same period. We find that poverty dynamics measured through poverty cycles differ for alternative poverty rate indicators. Another critical point is the magnitude of change in the poverty cycles. Prolonged and more volatile poverty cycles have a significant adverse impact on people and families facing them. That is particularly important for policymakers who should rethink poverty policy guidelines aimed at helping people with more volatile poverty cycles first. Our is the first study, to our knowledge, to isolate poverty cycles and focus on their nature. Poverty cycles should attract more attention from policymakers since they more accurately assess nations’ economic well-being than output (GDP).

Realignment and Macropartisanship

2001 ◽  
Vol 95 (4) ◽  
pp. 953-962 ◽  
Author(s):  
Michael F. Meffert ◽  
Helmut Norpoth ◽  
Anirudh V. S. Ruhil
Keyword(s):  
New Deal ◽  
Time Series Data ◽  
Party Identification ◽  
Series Data ◽  
Presidential Approval ◽  
Short Term ◽  
Partisan Realignment ◽  
Electoral Equilibrium ◽  
The New Deal ◽  
The U.S

Aggregate party identification (macropartisanship) has exhibited substantial movement in the U.S. electorate over the last half century. We contend that a major key to that movement is a rare, massive, and enduring shift of the electoral equilibrium commonly known as a partisan realignment. The research, which is based on time-series data that employ the classic measurement of party identification, shows that the 1980 election triggered a systematic growth of Republican identification that cut deeply into the overwhelming Democratic lead dating back to the New Deal realignment. Although short-term fluctuations in macropartisanship are responsive to the elements of everyday politics, neither presidential approval nor consumer sentiment is found responsible for the 1980 shift.

Use of Hydrologic Time Series Data for Analysis the Karstic Drought Characteristic

2014 ◽  
Vol 641-642 ◽  
pp. 127-131
Author(s):  
Li Hong Liu ◽  
Da Sheng Wang ◽  
He Huang ◽  
Guang Quan Xu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Water Volume ◽  
Significant Heterogeneity ◽  
Drought Duration ◽  
Hydrologic Time Series ◽  
Drought Hazard ◽  
Groundwater Drought ◽  
Drought Frequency Analysis

Karstification creates significant heterogeneity of hydraulic conductivity within the aquifer, where flows are organized to a hierarchical structure, from the surface to the spring. A karstic aquifer subjected to groundwater flood and drought, as a site for the occurrence of karst groundwater, is the main or unique focus for groundwater development and utilization in southwestern China. The present paper introduces a methodology devoted to groundwater drought hazard assessment. It focuses on groundwater drought by applying of the spring time series for an estimate and categorization of operating resources of groundwater. The results show that a permit for use of water for ER1+ER2 up to 0.48 m3/s, with the exceeding probability 80% selected for representing dry. The longest drought duration time was happened in the year 1993 with the 2.9×106m3 shortage of water volume. Groundwater drought frequency analysis provides a useful tool for water management.

scholarly journals Time Series Outlier Detection Based on Sliding Window Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yufeng Yu ◽  
Yuelong Zhu ◽  
Shijin Li ◽  
Dingsheng Wan
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Hydrologic Time Series ◽  
Prediction Confidence ◽  
Evaluation Of Data ◽  
Operation And Management

In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

Forecasting the Impact of Socioeconomic and Demographic Change on Product Demand

1985 ◽  
Vol 22 (4) ◽  
pp. 415-423 ◽  
Author(s):  
John M. Mccann ◽  
David J. Reibstein
Keyword(s):  
Time Series ◽  
Cross Sections ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Product Demand ◽  
New Methods ◽  
Differential Scheme ◽  
The Impact ◽  
The U.S

The U.S. population is expected to undergo significant shifts in its demographic and socioeconomic makeup. The authors present a series of methods for estimating the impact of these shifts on product demand. In addition, two new methods for pooling time-series and cross-sectional data are presented. One method combines disaggregate cross sectional data with aggregate time-series data and the second method involves a differential scheme for pooling cross sections for each variable in the model.

scholarly journals FLUKTUASI KURS RUPIAH TERHADAP DOLLAR AMERIKA SERIKAT PADA PERIODE TAHUN 1997.I – 2004.IV

2009 ◽  
Vol 10 (2) ◽  
pp. 234
Author(s):  
Anggyatika Mahda Kurnia ◽  
Didit Purnomo
Keyword(s):  
Money Supply ◽  
Time Series Data ◽  
Model Specification ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Significant Variable ◽  
Analysis Tool ◽  
Normality Test ◽  
Result Analysis ◽  
The U.S

This study aimed to analyze the fluctuation of the rupiah against the U.S. dollar. The data used in this study are quarterly time series data between the 1997.I to 2004.IV. Analysis tool used in this study is multiple linear regressions using the Error Correction Model (ECM). The results of this study concluded that variables such as exchange rates, inflation, SBI rate and the value of imports is stationary, only money supply variable that is not stationary. Based on the classical assumption was not found problem. Normality test showed normal distribution of Ut, tests of model specification with the Ramsey Reset test indicates the model used is linear. The coefficient of determination (R2) showed that approximately 90.5813 percent of the value of the rupiah against the U.S. dollar be explained by variables in the model. Result analysis by t test found that a significant variable is the money supply, inflation, and the value of imports.

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