scholarly journals Estimating Water Reflectance at Near-Infrared Wavelengths for Turbid Water Atmospheric Correction: A Preliminary Study for GOCI-II

2020 ◽  
Vol 12 (22) ◽  
pp. 3791
Author(s):  
Jae-Hyun Ahn ◽  
Young-Je Park
Keyword(s):  
Near Infrared ◽  
Accurate Result ◽  
Atmospheric Correction ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Turbid Waters ◽  
The Mean ◽  
Relationship Of ◽  
Spectral Relationship

Atmospheric correction is a fundamental process to remove the atmospheric effect from the top-of-atmosphere level. The atmospheric correction algorithm developed by the Korea Institute of Ocean Science and Technology employs a near-infrared (NIR) water reflectance model to deal with non-negligible NIR water reflectance over turbid waters. This paper describes the NIR water reflectance models using visible bands of the Second Geostationary Ocean Color Imager (GOCI-II). Whereas the previous GOCI uses the 660 nm band to estimate NIR water reflectance (SR660), GOCI-II uses additional 620 and 709 nm bands, which improves estimation of NIR water reflectance. We developed two reflectance models with the additional bands based on a spectral relationship of water reflectance (SR709) and a spectral relationship of inherent optical properties (SRIOP) from red to NIR wavelengths. A preliminary validation of these two reflectance models was performed using both simulations and an in situ dataset. The validation result showed that the mean absolute percentage error of the SR709 model compared with SR660 was reduced by approximately 6% and 10% at 745 and 865 nm, respectively. Moreover, the mean absolute percentage error of the SRIOP model compared with SR660 was reduced by approximately 12% and 16% at 745 and 865 nm, respectively. Note that SR709 produces the most accurate result when there is only one sediment type, and SRIOP shows the most accurate result when various sediment types exist. Users will be able to optionally select the appropriate NIR water reflectance models in the GOCI-II atmospheric correction process to enhance the accuracy of aerosol reflectance correction over turbid waters.

scholarly journals Novel Spatiotemporal Feature Extraction Parallel Deep Neural Network for Forecasting Confirmed Cases of Coronavirus Disease 2019

2020 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Yamin Shen ◽  
Ping-Huan Kuo ◽  
Yung-Hsiang Chen
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Absolute Error ◽  
Mean Absolute Percentage Error ◽  
The Other ◽  
Percentage Error ◽  
Model Assessment ◽  
Absolute Percentage Error ◽  
Spatiotemporal Feature ◽  
The Mean

AbstractThe coronavirus disease 2019 pandemic continues as of March 26 and spread to Europe on approximately February 24. A report from April 29 revealed 1.26 million confirmed cases and 125 928 deaths in Europe. This study proposed a novel deep neural network framework, COVID-19Net, which parallelly combines a convolutional neural network (CNN) and bidirectional gated recurrent units (GRUs). Three European countries with severe outbreaks were studied—Germany, Italy, and Spain—to extract spatiotemporal feature and predict the number of confirmed cases. The prediction results acquired from COVID-19Net were compared to those obtained using a CNN, GRU, and CNN-GRU. The mean absolute error, mean absolute percentage error, and root mean square error, which are commonly used model assessment indices, were used to compare the accuracy of the models. The results verified that COVID-19Net was notably more accurate than the other models. The mean absolute percentage error generated by COVID-19Net was 1.447 for Germany, 1.801 for Italy, and 2.828 for Spain, which were considerably lower than those of the other models. This indicated that the proposed framework can accurately predict the accumulated number of confirmed cases in the three countries and serve as a crucial reference for devising public health strategies.

Prediksi Kedatangan Turis Menggunakan Algoritma Weighted Exponential Moving Average

2020 ◽  
Vol 12 (2) ◽  
pp. 129-132
Author(s):  
Sherly Florencia ◽  
Alethea Suryadibrata
Keyword(s):  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Error Measurement ◽  
Absolute Percentage Error ◽  
Natural Beauty ◽  
Expenditure Data ◽  
The Mean ◽  
Tourism Expenditure ◽  
Using Data

Tourism is an important factor for the development of a country. Tourism can be used as a promotion to introduce natural beauty and cultural uniqueness. Government needs to predict how many tourists will come every year to do a planning. Therefore, an application is needed to help to predict the arrival of tourists in each country. In this paper, we use Weighted Exponential Moving Average (WEMA) method to predict the arrival of tourist, tourism expenditure in the country, and departure using data from 2008 to 2018. Error measurement is calculated using the Mean Absolute Percentage Error (MAPE). The result shows that the lowest average MAPE on arrival data with span 2 is at 3.28. The lowest average MAPE on tourism expenditure data with span 2 is at 3.99%. The result shows that the lowest average MAPE on departure data with span 2 is at 3.63%.

scholarly journals Fuzzy Inference System Mamdani dan the Mean Absolute Percentage Error (MAPE) untuk Prediksi Permintaan Dompet Pulsa pada XL Axiata Depok

2017 ◽  
Vol 2 (2) ◽  
pp. 97
Author(s):  
Mochammad Bagoes Satria Junianto
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Inference System ◽  
Absolute Percentage Error ◽  
Sample Data ◽  
The Mean

Kemajuan perkembangan teknologi informasi pada era globalisasi sekarang ini sangat pesat; hal ini menuntut setiap perusahaan untuk dapat saling bersaing dalam dunia bisnis yang dinamis dan penuh persaingan. Pada proses manjaemen permintaan dompet pulsa di XL Axiata cabang Depok memerlukan peramalan yang cukup matang agar dompet pulsa yang diminta kepada pusat tidak berlebihan atau tidak terlalu sedikit untuk menjaga kestabilan antara penjualan; persediaan dan jumlah permintaan. Untuk dapat melakukan peramalan yang lebih akurat; maka diperlukan suatu metode yang dapat menghitung ketidakpastian yang terjadi; dalam hal ini metode yang digunakan adalah dengan menggunakan Fuzzy inference system metode Mamdani untuk meramalkan jumlah permintaan dompet pulsa berdasarkan jumlah penjualan dan persediaan. Dengan 12 sample data untuk masing-masing sistem satuam yang digunakan hasil yang didapatkan yaitu dengan menggunakan Fuzzy inference system metode mamdani MAPE yang didapat sebesar 18;56% untuk Dompul XL 5k; 5;38% untuk Dompul XL 10k dan 14;2% untuk Dompul XL Rupiah.

Modeling of Fixed Joints Stiffness Based on Modified Least-Square

2014 ◽  
Vol 536-537 ◽  
pp. 1365-1368
Author(s):  
Ming De Duan ◽  
Hao Liang Feng ◽  
Kang Hua Liu ◽  
Jun Yong Lu
Keyword(s):  
Experimental Data ◽  
Regression Analysis ◽  
Relative Error ◽  
Machine Tools ◽  
Mean Absolute Percentage Error ◽  
Least Square ◽  
Percentage Error ◽  
Regression Equations ◽  
Absolute Percentage Error ◽  
The Mean

According to experimental data, the model of fixed Joints stiffness in machine tools was built by least square of relative error. The new regression equations were obtained by regression analysis. Compared to the original equations with Gaussian least-square, the relative error of new regression equations is within 3.5%, which reduces by 12.5% and the mean absolute percentage error (MAPE) decreases by 18.0%, 12.4%and 19.0% respectively.

scholarly journals Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Mahadi Muhammad ◽  
Sri Wahyuningsih ◽  
Meiliyani Siringoringo
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Logical Relationship ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Fuzzy Logical

ABSTRAKFuzzy time series (FTS) Lee adalah suatu metode peramalan yang digunakan ketika jumlah data historis yang tersedia sedikit, serta tidak mensyaratkan asumsi-asumsi tertentu yang harus terpenuhi. Metode ini menggunakan data historis berupa himpunan fuzzy yang berasal dari bilangan real atas himpunan semesta pada data aktual. FTS Lee adalah perkembangan dari FTS Song dan Chissom, FTS Cheng, serta FTS Chen. Pada penelitian ini dibahas penerapan FTS Lee pada data Nilai Tukar Petani Subsektor Peternakan (NTPT) di Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil peramalan NTPT di Kalimantan Timur pada bulan Januari 2020 dengan menggunakan FTS Lee. Langkah awal dalam penelitian ini yaitu menentukan himpunan semesta pembicaraan, langkah kedua menentukan banyaknya himpunan fuzzy, langkah ketiga mendefinisikan derajat keanggotaan himpunan fuzzy terhadap  dan melakukan fuzzyfikasi pada data aktual, langkah keempat membuat fuzzy logical relationship, langkah kelima membuat fuzzy logical relationship group, langkah keenam melakukan defuzzyfikasi sehingga diperoleh hasil peramalan, serta dilanjutkan dengan menghitung nilai mean absolute percentage error. Hasil penelitian menunjukkan bahwa peramalan menggunakan FTS Lee pada bulan Januari 2020 adalah 110,25. Nilai mean absolute percentage error pada  hasil peramalan dengan menggunakan FTS Lee adalah sangat baik.  ABSTRACTLee’s Fuzzy time series (FTS) is a forecasting method that is used when the number of historical data that available was small and does not require certain assumptions to be fulfilled. This method uses historical data in the form of fuzzy sets derived from real numbers over the set of universes in the actual data. FTS Lee is a development of FTS Song and Chissom, FTS Cheng, and FTS Chen. This research discusses the application of FTS Lee to the Exchange Rate of Farmers Subsectors Farm (ERFSF) in Kalimantan Timur. The purpose of this study was to obtain the results of ERFSF forecasting in Kalimantan Timur in January 2020 using FTS Lee. The first step during research is to determine the set of speech universes, the second step is to determine the number of fuzzy sets, the third step is to define the degree of fuzzy association membership and fuzzification on the actual data, the fourth step is to create a fuzzy logical relationship, the fifth step is to create a fuzzy logical relationship group, the sixth step is to perform defuzzification in order to obtain forecasting results, and continue by calculating the mean absolute percentage error value. The results showed that forecasting using FTS Lee in January 2020 was 110,25. The mean absolute percentage error value in forecasting results using FTS Lee is very good.

Peramalan terhadap Forex dengan Metode ARIMA Studi Kasus GBP/USD

2019 ◽  
Vol 11 (1) ◽  
pp. 6-10
Author(s):  
Michael Saputra Suryono ◽  
Raymond Oetama
Keyword(s):  
Knowledge Discovery ◽  
Foreign Exchange ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
R Software ◽  
Absolute Percentage Error ◽  
Knowledge Discovery In Database ◽  
The Mean

Forex or Foreign Exchange is trading a country's currency with another country's currency. The purpose of this study is basically to test the accuracy of ARIMA on the GBP/USD currency pair. In addition, this research is expected to provide the benefits of knowledge about forecasting using ARIMA. This study resulted in forecasting the GBP/USD currency pair within 1 month, per 6 months from January 2018 to June 2018 using the ARIMA method and R software. Data to be used are data taken from January 2013 to June 2018. For the the process will follow the process of the KDD (Knowledge Discovery in Database). The results obtained by the ARIMA model (3,2,1) as the best model to be applied for 1 month per 6 months on the GBP/USD currency pair because it has the lowest AIC value and the mean absolute percentage error is 3.16%.

Forecasting Technique for the Production Plan of the Local Earthenware Industries of Thailand

2014 ◽  
Vol 577 ◽  
pp. 1279-1282
Author(s):  
Weerapol Namboonruang ◽  
N Amdee
Keyword(s):  
Time Series ◽  
Classical Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Time Series Models ◽  
Production Plan ◽  
Absolute Percentage Error ◽  
The Mean

The purpose of this work is to compare the forecasting of time series models between two different models. One is the classical model and another is the Box-Jenkins model. The data are calculated using the circulation of Angbuaand Ahongwhich are the local earthenware products from Ratchaburi province, Thailand. Results show that the mean absolute percentage error (MAPE) of Angbua and Ahong are 17.80, 36.12 and 16.38,17.21 respectively. Also,prediction using the Box-Jenkins Model by ARIMA form of both products are (1, 0, 0) and (1, 1, 1). From this work the Box-Jenkins Model shows more appropriate method than the classical model considered by the less error.

scholarly journals Forecasting Tourist Arrival in the Province of Surigao del Sur, Philippines using Time Series Analysis

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Ruby Mae Ebuna Maliberan
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Forecast Model ◽  
Mean Absolute Percentage Error ◽  
Actual Data ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Increasing Trend ◽  
The Mean

The study attempted to forecast the number of tourist arrival in the province of Surigao del Sur using the historical monthly tourist arrival data from 2012-2016 using three time series. Findings showed that the tourist arrival in the province is likely to be increasing. As more foreign and local tourist arrivals are expected as a result of forecast model. Furthermore, it showed that there was a long term increasing trend of the tourist arrival in the province. Results revealed that the Mean Absolute Percentage Error (MAPE) of the forecasted tourist arrival data yielded an error of 11 % which means that predicted data is closer to the actual data. Based on the findings of the study, the researcher recommends that this study can be adapted by other Tourism Office of CARAGA, Philippines. 

scholarly journals Prediksi Jumlah Produksi Akibat Penyebaran Covid-19 Menggunakan Metode Fuzzy Takagi-Sugeno

2021 ◽  
Vol 5 (2) ◽  
pp. 220-230
Author(s):  
Khofifah Putriyani ◽  
Tenia Wahyuningrum ◽  
Yogo Dwi Prasetyo
Keyword(s):  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Food Company ◽  
Actual Production ◽  
The Mean ◽  
Bread Production ◽  
Takagi Sugeno

Global Bakery is a food company engaged in bread production that is having difficulty determining how much bread will be produced in the event of a pandemic. This study aims to help predict the amount of bread that will be produced during a pandemic. With the benefit of making it easier for companies to determine the amount of bread to be produced. Data obtained from Global Bakery and the official website of Covid-19 Bekasi Regency from March 20, 2020 to April 20, 2020. The author uses the Fuzzy Takagi-Sugeno method to predict the amount of bread that must be produced by Global Bakery during a pandemic with the following stages: fuzzification, rule formation, calculating ɑ-predicate and zi value, then calculating defuzification. Then an evaluation is carried out using the Mean Absolute Percentage Error (MAPE). This study uses Matlab's GUI tools in implementing the Predictor program. The Fuzzy Takagi-Sugeno method is able to predict the amount of bread production at Global Bakery with optimal results, where if the sales are 180 pieces, the remaining sales are 289, and the number of positive cases of Covid-19 is 6 people with the actual production number of 469 pieces, then The prediction results obtained were 347 units. The results of the calculations that have been done obtained the results of accuracy with a good category, namely with a MAPE value of 18.6%.  

scholarly journals Multiple-Forecasting Approach: A Prediction of CO2 Emission from the Paddy Crop in India

2021 ◽  
Author(s):  
Pawan Kumar Singh ◽  
Alok Kumar Pandey ◽  
Sahil Ahuja ◽  
Ravi Kiran
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Co2 Emission ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Paddy Crop

Abstract This paper compares four prediction methods namely Random Forest Regressor (RFR), SARIMAX, Holt-Winters (H-W), and the Support Vector Regression (SVR) to forecast the total CO2 emission from the paddy crop in India. The major objective of this study is to compare these four models to suggest an effective model to predict the total CO2 emission. Data from 1961 to 2018 has been categorised into two parts: training and test data. The study forecasts total CO2 emission from paddy crop in India from 2019 to 2025. A comparison of mean absolute percentage error (MAPE) and the mean square error (MSE), highlights the differences in accuracy among the four models. The mean absolute percentage error (MAPE) and the mean square error (MSE) for the four methods are: RFR (MAPE: 5.67; MSE: 549900.02), SARIMAX (MAPE:1.67; MSE:70422.35), H-W (MAPE:0.75; MSE:16648.58), and SVR (MAPE: 0.91; MSE: 17832.4). The values of MAPE and MSE with the Holt-Winters (H-W) and the Support Vector Regression (SVR) is relatively low as compared to SARIMAX and RFR. On the basis of these results, it can be inferred that H-W and SVR were found suitable models to forecast the total CO2 emission from paddy crop. Holt-Winters the model predicted 14364.97 for the year 2025 and SVR predicted 13696.67 for the year 2025. These predictions can be used by the decision-maker to build a suitable policy for future studies. For further research, this approach can be contrasted with other approaches, such as the Neural Network or other forecasting methods, using more important datasets to train the model to achieve better forecast accuracy.

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