scholarly journals Surface Roughness Estimation in the Orog Nuur Basin (Southern Mongolia) Using Sentinel-1 SAR Time Series and Ground-Based Photogrammetry

2020 ◽  
Vol 12 (19) ◽  
pp. 3200
Author(s):  
Tobias Ullmann ◽  
Georg Stauch
Keyword(s):  
Time Series ◽  
Surface Roughness ◽  
Root Mean Square ◽  
Land Surface ◽  
Arid Environment ◽  
Mean Square ◽  
Geomorphological Mapping ◽  
Multi Temporal ◽  
Dry Conditions ◽  
Single Field

This study demonstrates an application-oriented approach to estimate area-wide surface roughness from Sentinel-1 time series in the semi-arid environment of the Orog Nuur Basin (southern Mongolia) to support recent geomorphological mapping efforts. The relation of selected mono- and multi-temporal SAR features and roughness is investigated by using an empirical multi-model approach and selected 1D and 2D surface roughness indices. These indices were obtained from 48 high-resolution ground-based photogrammetric digital elevation models, which were acquired during a single field campaign. The analysis is backed by a time series analysis, comparing Sentinel-1 features to temporal-corresponding observations and reanalysis datasets on soil moisture conditions, land surface temperature, occurrence of precipitation events, and presence and development of vegetation. Results show that Sentinel-1 features are hardly sensitive to the changing surface conditions over none to sparsely vegetated land, indicating very dry conditions throughout the year. Consequently, surface roughness is the dominating factor altering SAR intensity. The best correlation is found for the combined surface roughness index Z-Value (ratio between the root mean square height and the correlation length) and the mean summer VH intensity with an r2 coefficient of 0.83 and an Root-Mean-Square Error of 0.032.

scholarly journals A Novel Method for Sea Surface Temperature Prediction Based on Deep Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xuan Yu ◽  
Suixiang Shi ◽  
Lingyu Xu ◽  
Yaya Liu ◽  
Qingsheng Miao ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Root Mean Square ◽  
Spatial Information ◽  
Sea Surface ◽  
The Yellow Sea ◽  
Percentage Error ◽  
Good Prediction ◽  
Mean Square

Sea surface temperature (SST) forecasting is the task of predicting future values of a given sequence using historical SST data, which is beneficial for observing and studying hydroclimatic variability. Most previous studies ignore the spatial information in SST prediction and the forecasting models have limitations to process the large-scale SST data. A novel model of SST prediction integrated Deep Gated Recurrent Unit and Convolutional Neural Network (DGCnetwork) is proposed in this paper. The DGCnetwork has a compact structure and focuses on learning deep long-term dependencies in SST time series. Temporal information and spatial information are all included in our procedure. Differential Evolution algorithm is applied in order to configure DGCnetwork’s optimum architecture. Optimum Interpolation Sea Surface Temperature (OISST) data is selected to conduct experiments in this paper, which has good temporal homogeneity and feature resolution. The experiments demonstrate that the DGCnetwork significantly obtains excellent forecasting result, predicting SST by different lengths flexibly and accurately. On the East China Sea dataset and the Yellow Sea dataset, the accuracy of the prediction results is above 98% on the whole and all mean absolute error (MAE) values are lower than 0.33°C. Compared with the other models, root mean square error (RMSE), root mean square percentage error (RMSPE), and mean absolute percentage Error (MAPE) of the proposed approach reduce at least 0.1154, 0.2594, and 0.3938. The experiments of SST time series show that the DGCnetwork model maintains good prediction results, better performance, and stronger stability, which has reached the most advanced level internationally.

scholarly journals Forecasting hourly emergency department arrival using time series analysis

2020 ◽  
Vol 26 (1) ◽  
pp. 34-43
Author(s):  
Avishek Choudhury ◽  
Estefania Urena
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Mean Square ◽  
Mean Error ◽  
Auto Regressive

Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

scholarly journals Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Ayub Mohammadi ◽  
Khalil Valizadeh Kamran ◽  
Sadra Karimzadeh ◽  
Himan Shahabi ◽  
Nadhir Al-Ansari
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Decision Trees ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Susceptibility Mapping ◽  
Mean Square ◽  
Topographic Wetness Index ◽  
Flood Detection ◽  
Flooded Areas

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

The Generating Mechanism of Surface Roughness by Random Cutting Edges

1978 ◽  
Vol 20 (4) ◽  
pp. 197-200
Author(s):  
M. Hasegawa ◽  
T. Tsukizoe
Keyword(s):  
Surface Roughness ◽  
Root Mean Square ◽  
Statistical Approach ◽  
Ground Surface ◽  
Root Mean Square Roughness ◽  
Mean Square ◽  
Two Dimensional ◽  
Dimensional Distribution

This paper describes a statistical approach for predicting the generating mechanism of the surface roughness produced by random cutting edges. The two-dimensional distribution of the generated surface roughness is derived by considering the distribution of the maxima of the cutting edges. The method is used to determine the root-mean-square roughness of the ground surface.

scholarly journals Investigating the Effects of Land Use and Land Cover on the Relationship between Moisture and Reflectance Using Landsat Time Series

2020 ◽  
Vol 12 (12) ◽  
pp. 1919
Author(s):  
Heather J. Tollerud ◽  
Jesslyn F. Brown ◽  
Thomas R. Loveland
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Great Plains ◽  
Satellite Data ◽  
Land Surface ◽  
Effective Moisture ◽  
Average Reflectance ◽  
Dry Conditions ◽  
The Relationship

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly ( p < 0.01 ) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships.

scholarly journals Study on Apparent Kinetic Prediction Model of the Smelting Reduction Based on the Time-Series

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Guo-feng Fan ◽  
Shan Qing ◽  
Hua Wang ◽  
Zhe Shi ◽  
Wei-Chiang Hong ◽  
...  
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Golden Section ◽  
Ar Model ◽  
Mean Square ◽  
Smelting Reduction ◽  
Data Mining Technique ◽  
Promising Alternative

A series of direct smelting reduction experiment has been carried out with high phosphorous iron ore of the different bases by thermogravimetric analyzer. The derivative thermogravimetric (DTG) data have been obtained from the experiments. One-step forward local weighted linear (LWL) method , one of the most suitable ways of predicting chaotic time-series methods which focus on the errors, is used to predict DTG. In the meanwhile, empirical mode decomposition-autoregressive (EMD-AR), a data mining technique in signal processing, is also used to predict DTG. The results show that (1) EMD-AR(4) is the most appropriate and its error is smaller than the former; (2) root mean square error (RMSE) has decreased about two-thirds; (3) standardized root mean square error (NMSE) has decreased in an order of magnitude. Finally in this paper, EMD-AR method has been improved by golden section weighting; its error would be smaller than before. Therefore, the improved EMD-AR model is a promising alternative for apparent reaction rate (DTG). The analytical results have been an important reference in the field of industrial control.

scholarly journals Peramalan Data Ekspor Nonmigas Provinsi Kalimantan Timur Menggunakan Metode Weighted Fuzzy Time Series Lee

2021 ◽  
Vol 14 (1) ◽  
pp. 1-10
Author(s):  
Muhammad Wahdeni Pramana ◽  
Ika Purnamasari ◽  
Surya Prangga
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Mean Square ◽  
Absolute Percentage Error

Ekspor merupakan aktivitas perdagangan atau penjualan barang dari dalam negeri ke luar negeri. Ekspor nonmigas sebagai salah satu komponen pembentuk Produk Domestik Regional Bruto (PDRB) sehingga perlu adanya suatu peramalan nilai di masa mendatang. Fuzzy Time Series (FTS) merupakan metode peramalan dengan berdasarkan teori himpunan fuzzy, logika fuzzy, serta hasil peramalan yang dapat dibahasakan (linguistik). Metode Weighted Fuzzy Time Series (WFTS) Lee merupakan perluasan dari metode FTS dengan penambahan pembobotan pada tiap pola relasi yang terbentuk. Tujuan penelitian ini adalah memperoleh nilai peramalan ekspor nonmigas Provinsi Kalimantan Timur pada bulan November 2020 serta memperoleh nilai akurasi peramalan berdasarkan metode Mean Absolute Percentage Error (MAPE) dan Root Mean Square Error (RMSE). Berdasarkan hasil analisis diperoleh nilai akurasi peramalan untuk data Ekspor Nonmigas Provinsi Kalimantan Timur bulan Januari 2019 – Oktober 2020 dengan konstanta pembobot   menggunakan metode MAPE diperoleh hasil keseluruhan dibawah 10% sehingga diperoleh konstanta pembobot terbaik yaitu  dengan nilai MAPE terminimum yaitu sebesar 3,62% dan RMSE minimum sebesar 50,67. Dari hasil tersebut, diperoleh hasil peramalan untuk bulan November 2020 dengan menggunakan kontanta pembobot terbaik  yaitu sebesar 850,96 juta USD.

scholarly journals INVESTIGATION ON DATA COLLECTION AND FRACTAL CHARACTERISTICS OF SOIL SURFACE ROUGHNESS

2020 ◽  
Vol 61 (2) ◽  
pp. 135-142
Author(s):  
Yi Qiu ◽  
Zhi Chen ◽  
Zhanfeng Hou ◽  
Haiyang Liu ◽  
Fang Guo ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Root Mean Square ◽  
Soil Surface ◽  
Laser Sensor ◽  
Mean Square ◽  
Roughness Index ◽  
Soil Surface Roughness ◽  
Fractal Characteristics ◽  
Two Parameters

It is of great significance to acquire the soil surface roughness accurately for the study of the interaction between tractors and soil. Based on the laser sensor, this paper proposed the non-contact measuring instrument of the soil surface roughness with the data acquiring system by using Lab-View software. By using W-M theory, three commonly used fractal dimension calculation methods are compared and analyzed.. The result showed that the Root-mean-square method has the highest accuracy and clear physical meaning, which is ideal method to calculate the soil surface roughness characteristics. When the fractal dimension is between 1.4 and 1.6, the acquired data is analysed by the Root-mean-square method to obtain the fractal features of the soil surface roughness. The experiment results indicated that the fractal dimension of the ploughed surface is 1.39, that of disc harrow surface is 1.550, and that of rolled surface is 1.46-1.54. Obviously, the fractal dimension can accurately distinguish the soil surface roughness with the different treatments. However, the fractal dimension selected from different scales showed an obvious instability during calculations. The surface roughness index combined with the two parameters can effectively represent the soil surface roughness, and the larger the surface roughness index is, the greater the surface roughness is.

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