scholarly journals Glacier Surface Velocities in the Chandrabhaga Massif, Western Himalaya (India) Derived Using COSI-Corr From Landsat Images

2021 ◽  
Author(s):  
Suresh Das
Keyword(s):  
Time Series Data ◽  
Morphological Characteristics ◽  
Maximum Velocity ◽  
Western Himalaya ◽  
Surface Velocity ◽  
Series Data ◽  
High Mountain ◽  
Landsat Images ◽  
Glacier Surface ◽  
The Future

Abstract Himalayan glaciers act as a storehouse of freshwater outside the polar region and sustain the flow of several Asia’s major river systems, such as the Ganges, Indus, and Brahmaputra. Glaciers in High Mountain Asia are likely to be affected by climate change, posing a threat to the future water supply. Frequent mapping and monitoring of these glaciers are in urgent need to assess the future water storage and glacier-climate interaction. In this context, mapping and monitoring of surface velocity can help to infer the health of a glacier. However, systematic assessment of glacier surface velocity is limited to fewer basins or individual glaciers in the Himalaya. Here, I have characterized the spatial and temporal velocity variations of glaciers in the Chandrabhaga massif (CBM) using Landsat time-series data spanning nearly the last three decades (1992-2019). Rigorous post-processing was performed to improve the accuracy of remote sensing derived velocity products. Glaciers showed heterogeneous spatial and temporal velocity patterns based on morphological characteristics and topographical settings. Heavily debris-covered tongues showed nearly stagnant flow (<10 m/yr) while maximum velocity (>60 m/yr) was observed for clean glaciers with steep gradients and crevasses. Surface velocity near the terminus of the lake terminating glaciers was almost double than land terminating debris-covered tongues (32.5 m/yr vs. 12.6 m/yr). An increasing trend in surface velocity is attributed to the rising trend in air temperature in western Himalaya.

scholarly journals GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic

2021 ◽  
Vol 5 (5) ◽  
pp. 619-635
Author(s):  
Harya Widiputra
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Short Term Memory ◽  
Human Mobility ◽  
Positive Association ◽  
Human Movement ◽  
Daily Basis ◽  
Series Data ◽  
Data Prediction ◽  
The Future

The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF

scholarly journals Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

2020 ◽  
Vol 20 (1) ◽  
pp. 19-29
Author(s):  
Minsu Jeong ◽  
Taesam Lee ◽  
JooHeon Lee ◽  
Hyeonseok Choi ◽  
Sunkwon Yoon
Keyword(s):  
Climate Change ◽  
Markov Chain ◽  
Differential Evolution ◽  
Frequency Analysis ◽  
Climate Models ◽  
Time Series Data ◽  
Global Climate Models ◽  
Series Data ◽  
Hourly Rainfall ◽  
The Future

In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

scholarly journals 30 m annual land cover and its dynamics in China from 1990 to 2019

2021 ◽  
Author(s):  
Jie Yang ◽  
Xin Huang
Keyword(s):  
Land Cover ◽  
Time Series Data ◽  
Impervious Surface ◽  
Google Earth ◽  
Third Party ◽  
Series Data ◽  
Fundamental Parameter ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Training Samples

Abstract. Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China Land Cover Dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics of China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China’s Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Using 335,709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial-temporal filtering and logical reasoning to further improve the spatial-temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5,463 visually-interpreted samples. A further assessment based on 5,131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC, and GlobaLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China’s LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease of cropland (−4.85 %) and grassland (−3.29 %), increase of forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g., Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at http://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

scholarly journals Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

2021 ◽  
Vol 12 ◽  
Author(s):  
Suran Liu ◽  
Yujie You ◽  
Zhaoqi Tong ◽  
Le Zhang
Keyword(s):  
Time Series ◽  
Predictive Model ◽  
Time Series Data ◽  
Chaotic Behavior ◽  
Flow Behavior ◽  
Series Data ◽  
High Dimensional ◽  
Future State ◽  
Disease Occurrence ◽  
The Future

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

scholarly journals Impact of urban evolution on local temperature trends of Rawalpindi and Islamabad

2020 ◽  
Vol 2 (2) ◽  
pp. 33-46
Author(s):  
Sajjad Hussain Sajjad ◽  
Khuram Shahzad ◽  
Tasawar Iqbal ◽  
Nasir Ashraf
Keyword(s):  
Urban Growth ◽  
Time Series Data ◽  
Local Temperature ◽  
Series Data ◽  
Twin Cities ◽  
Landsat Images ◽  
Barren Land ◽  
Temperature Trends ◽  
Surface Method ◽  
Supervised Image Classification

Rawalpindi and Islamabad commonly known as twin cities of Pakistan have 3.2 million population. Twin cities have rapidly urbanized in the last three decades. The objective of the present study is to compute the urban growth and its effect on evolution of local temperature trends of twin cities. To compute the land-cover change such as built-up area, vegetation cover, water and barren land, Landsat images of 1980, 1992, 2000 and 2013 are classified by using the supervised image classification with maximum likelihood rule and probability surface method. To evaluate the change in temperature trends, homogenized time series data of daily averaged monthly minimum (Tmin) and maximum (Tmax) temperatures for the period of 1983 to 2013 is analyzed by using the linear regression. The results show that built-up area of twin cities increased from 66 km2 in 1983 to 148 km2 in 2013 with an increase of 120 per cent within 31 years. Due to resulted urbanization, Tmin and Tmax of twin cities have been increasing. Tmin is increased more in Rawalpindi than Islamabad and Tmax is increased more in Islamabad than Rawalpindi. The highest increase in Tmin and Tmax at both stations is observed during spring season.

scholarly journals Future trends of COVID-19 disease outbreak in different states in India: a periodic regression analysis

2020 ◽  
Author(s):  
Bharath Prasad Cholanayakanahalli Thyagaraju ◽  
Srikantha Gowda ◽  
Sharanagouda Patil ◽  
Chandrashekar Srikantiah ◽  
Kuralayanapalya Puttahonnappa Suresh
Keyword(s):  
Tamil Nadu ◽  
Time Series Data ◽  
Human Life ◽  
Disease Outbreak ◽  
Series Data ◽  
Indian Government ◽  
Economic Wealth ◽  
The Future ◽  
Periodic Regression ◽  
The Impact

COVID-19 (Coronavirus disease 19) is the deadliest pandemic, and by August 2, >18.2 million population worldwide were infected with SARS-CoV-2 virus causing burden on human life and economic loss. Disease outbreak analysis has become a priority for the Indian government to initiate necessary healthcare measures in lowering the impact of this deadly pandemic viral disease. In this study, time series data for COVID-19 disease was extracted from the website www.covid19india.org, analysed by using periodic regression model, the expected number of cases till 02 October 2020 was predicted and to develop a stochastic models using periodic regression in the top 15 highly infected states in India. The analysis reported increasing pattern at initial days of prediction and showed a decreasing trend for the number of reporting cases, which may reduce in future days for states like West Bengal, Karnataka, Uttar Pradesh, Bihar, Telangana, Assam and Odisha. However, for the states of Maharashtra, Tamil Nadu, Gujarat, Rajasthan, Haryana and Madhya Pradesh, showed a rapid phase of increase in disease outbreak that is likely to infect more population and indicates the pandemic nature of this disease over a period. Presently, Delhi shows a drastic reduction in the number of cases, that may increase in the future, which can be controlled if appropriate preventive measures are followed strictly and effectively. Our model highlights that continuous and constant efforts are needed for the prevention of new infections of the disease in all states that helps to effectively mitigate the disease and to allocate scarce resources effectively in the future that could improve the economic wealth in India.

Developing Public Sector Growth through E-Government and Democracy A Detailed Analysis

2020 ◽  
pp. 8-15
Author(s):  
Taufiq Gutawa
Keyword(s):  
Public Sector ◽  
Time Series Data ◽  
Series Data ◽  
Control Variables ◽  
Review Section ◽  
The Future ◽  
Efficiency And Effectiveness ◽  
Asean Countries ◽  
The Government ◽  
The Right

Public sector growth refers to the growth and development in the government-controlled departments and establishments. The industries and different sectors of a country that come under the influence of government come under public sector. E-government is actually the employment of innovative techniques and practices while performing several operations for the facilitation of citizens by the government. The core motive of using e-government practices to ensure the efficiency and effectiveness of those operations that are being performed for the public. Democracy refers to the right of citizens of a particular country in order to choose the leaders or government of their own choice based on the decision of majority. This study investigates promoting public sector growth through E-government adoption and democracy in ASEAN countries. Transparency rate and population factor are two important control variables which are induced in this research study. In the literature review section, previous related research studies have been indicated. The time-series data has been collected about concerned variables regarding ASEAN countries. The analyses portion includes unit root IPS, Pedroni cointegration and FMOLS regression and concluded that the hypotheses proposed by the researcher are accepted along with some share of impact of control variables. The researcher concluded that E-government and democracy positively impact public sector growth of ASEAN countries. At the last of this study, implications, limitations and future recommendations are also present. The implications include various theoretical, practical and policy making contexts. The future recommendations can be used by the future researchers so that they can increase the scope of their researches.

Early 21st-century glacier surface mass balance across High Mountain Asia derived from remote sensing

2020 ◽  
Author(s):  
Evan Miles ◽  
Michael McCarthy ◽  
Amaury Dehecq ◽  
Marin Kneib ◽  
Stefan Fugger ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mass Balance ◽  
Surface Velocity ◽  
High Mountain ◽  
Surface Mass Balance ◽  
Equilibrium Line Altitude ◽  
Glacier Surface ◽  
Surface Mass ◽  
Early 21St Century ◽  
Debris Cover

&lt;p&gt;Glaciers in High Mountain Asia have experienced intense scientific scrutiny in the past decade due to their hydrological and societal importance. The explosion of freely-available satellite observations has greatly advanced our understanding of their thinning, motion, and overall mass losses, and it has become clear that they exhibit both local and regional variations due to debris cover, surging and climatic regime. However, our understanding of glacier accumulation and ablation rates is limited to a few individual sites, and altitudinal surface mass balance is essentially unknown across the vast region.&lt;/p&gt;&lt;p&gt;Here we combine recent assessments of ice thickness and surface velocity to correct observed glacier thinning rates for mass redistribution in a flowband framework to derive the first estimates of altitudinal glacier surface mass balance across the region. We first evaluate our results at the glacier scale with all available glaciological field measurements (27 glaciers), then analyze 4665 glaciers (we exclude surging and other anomalous glaciers) comprising 43% of area and 36% of mass for glaciers larger than 2 km&lt;sup&gt;2&lt;/sup&gt; in the region. The surface mass balance results allow us to determine the equilibrium line altitude for each glacier for the period 2000-2016.&amp;#160; We then aggregate our altitudinal and hypsometric surface mass balance results to produce idealised profiles for distinct subregions, enabling us to consider the subregional heterogeneity of mass balance and the importance of debris-covered ice for the region&amp;#8217;s overall ablation.&lt;/p&gt;&lt;p&gt;We find clear patterns of ELA variability across the region. &amp;#160;9% of glaciers accumulate mass over less than 10% of their area on average for the study period. These doomed&amp;#160; glaciers are concentrated in Nyainqentanglha, which also has the most negative mass balance of the subregions, whereas accumulation area ratios of 0.7-0.9 are common for glaciers in the neutral-balance Karakoram and Kunlun Shan. We find that surface debris extent is negatively correlated with ELA, explaining up to 1000 m of variability across the region and reflecting the importance of avalanching as a mass input for debris-covered glaciers at lower elevations. However, in contrast with studies of thinning rates alone, we find a clear melt reduction for low-elevation debris-covered glacier areas, consistent across regions, largely resolving the &amp;#8216;debris cover anomaly&amp;#8217;.&amp;#160;&amp;#160;&lt;/p&gt;&lt;p&gt;Our results provide a comprehensive baseline for the health of the High Asian ice reservoirs in the early 21&lt;sup&gt;st&lt;/sup&gt; Century. The estimates of altitudinal surface mass balance and ELAs will additionally enable novel strategies for the calibration of glacier and hydrological models. Finally, our results emphasize the potential of combined remote-sensing observations to understand the environmental factors and physical processes responsible for High Asia&amp;#8217;s heterogeneous patterns of recent glacier evolution.&lt;/p&gt;

Analysis of the International Gold Market State Based on Markov Chain

2014 ◽  
Vol 687-691 ◽  
pp. 4946-4949
Author(s):  
Ying Cheng ◽  
Kai Tan ◽  
Qiao Wang ◽  
Dong Fang Zhao
Keyword(s):  
Markov Chain ◽  
Time Series Data ◽  
Return Rate ◽  
Series Data ◽  
Related Information ◽  
Gold Market ◽  
Fluctuation Rate ◽  
The Future ◽  
Market State ◽  
Financial Environment

This paper focuses on the time series data about the closing price of international gold for 84 trading days between December 2013 and March 2014. The related information is from Shanghai Gold Exchange. Firstly, the analysis concerning fluctuations of return rate about the international gold market is conducted through various descriptive statistics such as skewness, kurtosis and etc. Then, the Markov Chain is applied in distinguishing the five states in international gold market, as well as the shifts and patterns among them. And these states are reasonably subdivided by the return rate of international market. The analysis implies that during the stable period of the international financial environment, the shock market will take the lead, with the international gold market continue to fluctuate. To be specific, 38% of the future will be featured by a fluctuation rate between-0.5% and 0.5%; 28% of the future time the gold price will go down, and 34% of the time it will go up.

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