scholarly journals Investigation of Sentinel-1-derived land subsidence using wavelet tools and triple exponential smoothing algorithm in Lagos, Nigeria

2021 ◽  
Vol 80 (21) ◽  
Author(s):  
Femi Emmanuel Ikuemonisan ◽  
Vitalis Chidi Ozebo ◽  
Olawale Babatunde Olatinsu
Keyword(s):  
Land Subsidence ◽  
Exponential Smoothing ◽  
Smoothing Algorithm

scholarly journals Investigation of Sentinel-1 Derived Land Subsidence Using Wavelet Tools And Triple Exponential Smoothing Algorithm In Lagos, Nigeria

2021 ◽  
Author(s):  
Femi Emmanuel Ikuemonisan ◽  
Vitalis Chidi Ozebo ◽  
Olawale Babatunde Olatinsu
Keyword(s):  
Soil Moisture ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Exponential Smoothing ◽  
Future Trend ◽  
Smoothing Algorithm ◽  
Groundwater Abstraction ◽  
Groundwater Level Fluctuations ◽  
History Of ◽  
Sbas Insar

Abstract Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate private borehole and water well proliferation. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series is computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between –94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm which is approximately 23% increase.

scholarly journals Forecast of COVID-19 Cases in Indonesia with the Triple Exponential Smoothing Algorithm

2020 ◽  
Vol 23 (2) ◽  
pp. 61
Author(s):  
Ruliyanta Ruliyanta ◽  
Endang Retno Nugroho
Keyword(s):  
Task Force ◽  
Exponential Smoothing ◽  
Data Sources ◽  
Protective Equipment ◽  
Smoothing Algorithm ◽  
National Data ◽  
Deadly Disease ◽  
Risk Treatment ◽  
Real Time Information ◽  
Time Information

<em>The Coronavirus </em>(SARS-CoV-2)<em>, also known as </em>COVID-19<em>, has brought a worldwide threat to the living. The whole world is making extraordinary efforts to combat the spread of this deadly disease in terms of infrastructure, finances, data sources, protective equipment, life risk treatment, and several other resources. Artificial intelligence researchers focus their knowledge of expertise on developing mathematical models to analyze this epidemic situation using shared national data. To contribute to the welfare of the living community, this article proposes to utilize the Triple Exponential Smoothing algorithm to predict the development of </em>COVID-19<em> throughout the country by utilizing real-time information from the Task Force for the Acceleration of Handling of Coronavirus Disease </em>2019<em> in Indonesia. Based on forecasting results, in Indonesia by the end of </em>2020, COVID-19<em> will continue to grow significantly, the number of confirmed </em>COVID-19<em> people is </em>386,571<em> people with a death toll of </em>15,622<em>.</em>

scholarly journals Prediksi Stok dan Pengaturan Tata Letak Barang Menggunakan Kombinasi Algoritma Triple Exponential Smoothing dan FP-Growth

2020 ◽  
Vol 7 (5) ◽  
pp. 869
Author(s):  
Kristoko Dwi Hartomo ◽  
Sri Yulianto Prasetyo ◽  
Rahmat Abadi Suharjo
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rules ◽  
User Behavior ◽  
Exponential Smoothing ◽  
Behavior Patterns ◽  
Retail Sector ◽  
Smoothing Algorithm ◽  
Sales Data

<p>Persaingan bisnis semakin meningkat khususnya dalam bidang <em>retail</em>. Hal ini mengharuskan pemilik melakukan inovasi terhadap bisnisnya. Salah satu hal yang perlu diperhatikan oleh pemilik untuk mempertahankan dan menambah konsumen yaitu dengan melakukan pendekatan dengan konsumen. Pendekatan pada konsumen digunakan untuk mengenali dan memahami perilaku, kebutuhan dan keinginan konsumen. Pemilik swalayan ingin melakukan inovasi untuk melakukan perbaikan tata letak barang dan perbaikan stok, karena konsumen seringkali mengalami kesulitan dalam pencarian barang dan pihak swalayan sering mengalami kekurangan dan kelebihan stok barang. Berdasarkan permasalahan tersebut, maka tujuan penelitian adalah untuk mengoptimalkan pengaturan tata letak barang dan optimalisasi persediaan stok barang. Dalam penelitian ini menggunakan data penjualan yang diolah sehingga menghasilkan informasi untuk pemilik swalayan. Pengolahan data dalam penelitian ini disebut <em>data mining</em> dengan menggunakan algoritma <em>FP-Growth</em> dan <em>Triple Exponential Smoothing.</em> Algoritma <em>FP-Growth </em>digunakan untuk mengetahui pola perilaku konsumen sehingga dapat digunakan untuk pengambilan keputusan dalam penyusunan barang dan algoritma <em>Triple Exponential Smoothing </em>yang merupakan algoritma prediksi digunakan untuk pengaturan stock barang. Dalam penelitian ini dengan menggunakan algoritma <em>FP-Growth</em> menemukan 12 aturan asosiasi, aturan asosiasi yang memiliki nilai <em>lift ratio</em> paling tinggi adalah Teh dan gula dengan nilai <em>lift ratio </em>6.131 dan dengan algoritma <em>Triple Exponential Smoothing</em> diperoleh hasil prediksi pada bulan Januari 2018 adalah 131,141 Kg dengan tingkat akurasi MAPE 88,3 %.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Business competition is increasing especially in the retail sector. This requires the owner to innovate his business. The shop owner wants to make an invasion to repair goods and equipment, because consumers are in dire need of things and supermarkets often occur. One of the things that need to be considered by the owner to maintain and add consumers is by approaching consumers. Use of information to recognize and understand consumer needs and desires. By overcoming it, the purpose of the research is to regulate the procedures for goods and optimize the preparation of stock items. In this study using processed sales data. Information on information for shop owners. Data processing in this research is called data mining using FP-Growth and Triple Exponential Smoothing algorithms. FP-Growth algorithm to find out user behavior patterns can be used to develop Triple Exponential Smoothing decisions and algorithms which are forecasting algorithms for inventory items. In this study using the algorithm FP-Growth found 12 association rules, which have the highest lift ratio is Tea and sugar with a lift ratio of 6.131 and with Triple Exponential Smoothing algorithm, the forecasting result in January 2018 is 131.141 Kg with 88,3 % MAPE accuracy.</em></p><p><em><strong><br /></strong></em></p>

ANALISA ALGORITMA EXPONENTIAL SMOOTHING UNTUK SISTEM PERENCANAAN PRODUKSI KAIN BATIK DI UKM BATIK TINCTORI NATURAL DYE JAMBU

2019 ◽  
Vol 10 (1) ◽  
pp. 23-32
Author(s):  
Agustinus Budi santoso ◽  
Robby Andika Kusumajaya
Keyword(s):  
Digital Technology ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Natural Dye ◽  
Economic Science ◽  
Smoothing Algorithm ◽  
Digital Information ◽  
Exponential Smoothing Method ◽  
Customer Orders

Abstract Digital information in the economic science developed in the upstream and downstream sectors. Developments are followed by many UKM using digital technology in marketing. The forecasting uses the Exponential Smoothing algorithm Exponential Smoothing method is a procedure of continuous repairs on the latest forecasting of data. UKM Batik Tinctori Natural Dye is one of the batik producers who produce batik from customer orders and fulfill distributor requests from batik trendsThe demand of customers and distributors has an effect on the number of production batik of Tinctori Natural Dye UKM which has resulted in any batik trend that is in demand in the following month. Forecasting is calculated using the Exponential Smoothing method and Eviews software and manual comparison to lead the accuracy of the forecasting value. This prediction can be used to predict the production of batik types from the demands of consumers and distributors. Forecasting uses several methods to make predictions using different alpha and beta constants. The Comparisons of forcasting for next three months 13, 14, 15 and forecast comparisons of original values by forecasting data that already exists three months before 10, 11, 12 to find better constants that can approach the original values.

scholarly journals PERBANDINGAN MODEL SIR (SUSCEPTIBLE, INFECTIOUS, RECOVERED), EXPONENTIAL MOVING AVERAGE DAN SINGLE EXPONENTIAL SMOOTHING PADA PERAMALAN COVID-19

2021 ◽  
pp. 75-82
Author(s):  
Ade Bastian ◽  
Diana Surya Heriyana ◽  
Sandi Fajar Rodiansyah
Keyword(s):  
Model Test ◽  
Moving Average ◽  
Sir Model ◽  
Exponential Smoothing ◽  
Smoothing Algorithm ◽  
Shortness Of Breath ◽  
Test Results ◽  
New Type ◽  
Novel Coronavirus ◽  
Single Exponential

Novel Coronavirus 2019 (COVID-19) is a disease caused by SARS-CoV-2, COVID-19 is a new type of coronavirus that can be transmitted from human to human. This virus can cause pneumonia, which is inflammation of the lung tissue that causes impaired oxygen exchange, resulting in shortness of breath. Currently it is not known when the Covid-19 pandemic will end, therefore a forecast is needed to predict the spread of Covid-19. This forecasting uses the SIR (Susceptible, Infectious, Recovered), Exponential Moving Average and Single Exponential Smoothing algorithm. Of the three algorithms, which data will be most suitable for forecasting the spread of covid-19 in Indonesia will be compared. The conclusion of the SIR model test results with the PSBB variable inhibits the spread of the virus, the exponential moving average test gets an error value of 24.28% and exponential smoothing gets an error value of 40.07%. So the suitable algorithms used for covid-19 data are the sir model and the exponential moving average.

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