scholarly journals Coastal Image Classification and Pattern Recognition: Tairua Beach, New Zealand

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7352
Author(s):  
Bo Liu ◽  
Bin Yang ◽  
Sina Masoud-Ansari ◽  
Huina Wang ◽  
Mark Gahegan
Keyword(s):  
Time Series Data ◽  
Classification Model ◽  
Series Data ◽  
Coastal Morphology ◽  
State Classification ◽  
Tidal Data ◽  
Practical Constraints ◽  
Stable Classification ◽  
Term Data

The study of coastal processes is critical for the protection and development of beach amenities, infrastructure, and properties. Many studies of beach evolution rely on data collected using remote sensing and show that beach evolution can be characterized by a finite number of “beach states”. However, due to practical constraints, long-term data displaying all beach states are rare. Additionally, when the dataset is available, the accuracy of the classification is not entirely objective since it depends on the operator. To address this problem, we collected hourly coastal images and corresponding tidal data for more than 20 years (November 1998–August 2019). We classified the images into eight categories according to the classic beach state classification, defined as (1) reflective, (2) incident scaled bar, (3) non-rhythmic, attached bar, (4) attached rhythmic bar, (5) offshore rhythmic bar, (6) non-rhythmic, 3-D bar, (7) infragravity scaled 2-D bar, (8) dissipative. We developed a classification model based on convolutional neural networks (CNN). After image pre-processing with data enhancement, we compared different CNN models. The improved ResNext obtained the best and most stable classification with F1-score of 90.41% and good generalization ability. The classification results of the whole dataset were transformed into time series data. MDLats algorithms were used to find frequent temporal patterns in morphology changes. Combining the pattern of coastal morphology change and the corresponding tidal data, we also analyzed the characteristics of beach morphology and the changes in morphodynamic states.

Predicting Time Series from Short-Term High-Dimensional Data

2014 ◽  
Vol 24 (12) ◽  
pp. 1430033 ◽  
Author(s):  
Huanfei Ma ◽  
Tianshou Zhou ◽  
Kazuyuki Aihara ◽  
Luonan Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
High Dimensional Data ◽  
Series Data ◽  
High Dimensional ◽  
Short Term ◽  
Low Dimensional ◽  
Low Dimensional Dynamics ◽  
Term Data

The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.

scholarly journals Possible ecosystem impacts of applying MSY policies from single-species assessment

2005 ◽  
Vol 62 (3) ◽  
pp. 558-568 ◽  
Author(s):  
Carl J. Walters ◽  
Villy Christensen ◽  
Steven J. Martell ◽  
James F. Kitchell
Keyword(s):  
Time Series Data ◽  
Single Species ◽  
Maximum Sustainable Yield ◽  
Series Data ◽  
Ecosystem Structure ◽  
Widespread Application ◽  
Predator Species ◽  
Ecosystem Impacts ◽  
Term Data

Abstract Ecosim models have been fitted to time-series data for a wide variety of ecosystems for which there are long-term data that confirm the models' ability to reproduce past responses of many species to harvesting. We subject these model ecosystems to a variety of harvest policies, including options based on harvesting each species at its maximum sustainable yield (MSY) fishing rate. We show that widespread application of single-species MSY policies would in general cause severe deterioration in ecosystem structure, in particular the loss of top predator species. This supports the long-established practice in fisheries management of protecting at least some smaller “forage” species specifically for their value in supporting larger piscivores.

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

scholarly journals Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3553
Author(s):  
Jeremy Watts ◽  
Anahita Khojandi ◽  
Rama Vasudevan ◽  
Fatta B. Nahab ◽  
Ritesh A. Ramdhani
Keyword(s):  
Parkinson’S Disease ◽  
Time Series ◽  
Parkinson's Disease ◽  
Random Forest ◽  
Treatment Planning ◽  
Time Series Data ◽  
Classification Model ◽  
Series Data ◽  
Demographic Information ◽  
Subjective Data

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

Does Public Debt Service Expenditure Crowd-Out Economic Growth? Empirical Evidence from an African Developing Country

2019 ◽  
Vol 64 (3) ◽  
pp. 23-38
Author(s):  
Talknice Saungweme ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Public Debt ◽  
Time Series Data ◽  
Policy Formulation ◽  
Debt Service ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
The Impact

Abstract This paper contributes to the ongoing debate on the impact of public debt service on economic growth; and it provides an evidence-based approach to public policy formulation in Zimbabwe. The empirical analysis was performed by applying the autoregressive distributed lag (ARDL) technique to annual time-series data from 1970 to 2017. The study findings reveal that the impact of public debt service on economic growth in Zimbabwe is negative in the short run but positive in the long run. The results are suggestive of the existence of a crowding-out effect of public debt service in Zimbabwe in the short run and a crowding-in effect in the long run. In view of these findings, the government should consider fiscal and financial policies that promote a constant supply of long-term finance, long-term fixed investments, and extension of a government securities maturity structure so as to ensure sustainable short- and long-term public debt service expenditures. The study further recommends the strengthening of non-distortionary revenue mobilisation reforms to reduce market distortions and boost domestic investment.

scholarly journals Beyond the implications of Grand Ethiopian Renaissance Dam filling policies

2021 ◽  
Vol 7 (3) ◽  
pp. 313-330
Author(s):  
Abay Yimere ◽  
◽  
Engdawork Assefa ◽  
Keyword(s):  
Time Series Data ◽  
Water Levels ◽  
Series Data ◽  
Future Climate Change ◽  
Policy Debate ◽  
International Treaty ◽  
Term Operation ◽  
Operating Level ◽  
Minimum Operating

<abstract> <p>The Grand Ethiopian Renaissance Dam (GERD) in Ethiopia and High Aswan Dam (HAD) in Egypt both operate on the Nile River, independent of a governing international treaty or agreement. As a result, the construction of the GERD, the Earth's eighth largest dam, ignited a furious debate among Ethiopia, Sudan, and Egypt on its filling policies and long-term operation. Ethiopia and Egypt's stance on the Nile River's water resources, combined with a nationalistic policy debate on the GERD's filling policies and long-term operation, has severely affected progress toward reaching agreeable terms before the first round of GERD filling was completed. These three countries continue to debate on the terms of agreement for the second round of GERD filling, scheduled to start by July 2021. We examined the GERD filling strategy for five- and six-year terms using time series data for the periods 1979–1987 and 1987–1992 to combine analyses for dry and wet seasons and investigate the potential impacts of filling the GERD above the downstream HAD using four HAD starting water levels. A model calibrated using MIKE Hydro results shows that during both five- and six-year terms of future GERD filling, Egypt would not need to invoke the HAD's minimum operating level. We pursued a narrative approach that appeals to both a technical and non-technical readership, and our results show the urgent need for cooperation at both policy and technical levels to mitigate and adapt to future climate change through the development of climate-proof agreements. Moreover, the results call for the riparian countries to move away from the current nationalistic policy debate approach and pursue a more cooperative, economically beneficial, and climate adaptive approach.</p> </abstract>

scholarly journals THE TESTING OF MONEY NEUTRALITY IN ECONOMIC GROWTH OF INDONESIA

2017 ◽  
Vol 1 (1) ◽  
pp. 12
Author(s):  
Muammil Sun’an ◽  
Amran Husen
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Output Growth ◽  
Broad Sense ◽  
Series Data ◽  
Analysis Tool ◽  
Real Sector ◽  
Short Term ◽  
Correction Model

<p>This study aim is to test the money neutrality in a narrow sense (M1) and a broad sense (M2) to the growth of output (GDP) in Indonesia, both in short term and long term. This research uses quarterly time series data at 2010 - 2016 periods. The analysis tool used is Error Correction Model (ECM). The results show that short-term money supply (M1 and M2) affect on output growth. However, in the long term, only money circulation in a broad sense (M2) affects on output growth, which also means that money is not neutral because it affects the real sector (GDP).</p><p> <strong>Keywords:</strong> M1, M2, Population, Capital, and Economic Growth.</p>

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