Application of random forest time series, support vector regression and multivariate adaptive regression splines models in prediction of snowfall (a case study of Alvand in the middle Zagros, Iran)

2017 ◽  
Vol 134 (3-4) ◽  
pp. 769-776 ◽  
Author(s):  
Omid Hamidi ◽  
Leili Tapak ◽  
Hamed Abbasi ◽  
Zohreh Maryanaji
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Support Vector Regression ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Regression Splines ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

scholarly journals Prediksi Jumlah Pengiriman Barang Menggunakan Kombinasi Metode Support Vector Regression, Algoritma Genetika dan Multivariate Adaptive Regression Splines

2020 ◽  
Vol 7 (6) ◽  
pp. 1169
Author(s):  
Nendi Nendi ◽  
Arief Wibowo
Keyword(s):  
Support Vector Regression ◽  
Mean Absolute Percentage Error ◽  
Multivariate Adaptive Regression Splines ◽  
Percentage Error ◽  
Support Vector ◽  
Regression Splines ◽  
Absolute Percentage Error ◽  
Logistics Company ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

<p>Sektor usaha logistik telah berkembang sangat pesat di Indonesia saat ini. PT. XYZ  adalah sebuah perusahaan logistik yang menyediakan jasa pengiriman barang dari satu tempat menuju ke tempat yang lain. Sebagai perusahaan logistik dengan jumlah kendaraan 2.100 unit armada truk dan akan terus bertambah seiring dengan target yang dicanangkan perusahaan, dimana pada 2020 jumlah armada truk harus mencapai 6.000 unit truk. Saat ini strategi operasional logistik dihasilkan berdasarkan pengalaman dari steakholder. Hal ini tentu tidak bisa dipertanggung jawabkan secara ilmiah. Prediksi jumlah pengiriman barang harian dapat menjadi solusi dalam membantu perusahaan dalam merencanakan, memonitoring dan mengevaluasi strategi operasional logistik. Hasil pengujian menunjukkan penggabungan metode <em>Support Vector Regression</em> (SVR), algoritma genetika dan <em>Multivariate Adaptive Regression Splines </em>(MARS) dapat menghasilkan prediksi jumlah pengiriman barang harian dengan nilai <em>Mean Absolute Percentage Error</em> (MAPE) yaitu 0.0969% dengan parameter <em>epsilon</em>(𝜀) 1.92172577675873E-20, <em>complexitas</em>(𝑐) 62 dan <em>gamma</em>(γ) 1.0.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Abstract"><em>The logistics business sector has developed very rapidly in Indonesia today. PT XYZ is a national logistics company that provides freight forwarding services from one place to another. As a national-scale logistics company, the company is supported by a fleet of 2,100 trucks. The number of fleets will continue to grow in line with the target set by the company, namely in 2020 the number of truck fleets must reach 6,000 trucks. Currently the logistics operational strategy is produced based on stakeholder experience, this certainly causes problems in the company's overall operations. Prediction of the number of daily goods shipments can be a solution in helping companies in planning, monitoring and evaluating logistical operational strategies, based on the company's ability in the availability of a fleet of vehicles for shipping. This study proposes a combination of Support Vector Regression (SVR) methods, genetic algorithms and Multivariate Adaptive Regression Splines (MARS) for problem solving in the prediction process, including in the selection of appropriate training data. The test results show that the combination of the three methods can produce predictions of the number of daily shipments with values of Mean Absolute Percentage Error (MAPE) 0.0969%, epsilon (𝜀) 1.92172577675873E- 20, complexity (𝑐) 62, and gamma (γ) 1.0.</em></p><p class="Judul2"><strong><em><br /></em></strong></p>

scholarly journals Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

2020 ◽  
pp. 1-14
Author(s):  
Mohammed Okoe Alhassan ◽  
Michael Boakye Osei
Keyword(s):  
Random Forest ◽  
Polymethyl Methacrylate ◽  
Polymeric Materials ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Regression Splines ◽  
Soft Computing Techniques ◽  
Flammability Characteristics ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Soft-computing techniques for fire safety parameter predictions in flammability studies are essential for describing a material fire behaviour. This study proposed, two novel Artificial Intelligence developed models, Multivariate Adaptive Regression Splines (MARS) and Random Forest (RF) methods, to model and predict peak heat release rate (pHRR) of Polymethyl methacrylate (PMMA) from Microscale Combustion Calorimetry (MCC) experiment. From the statistical analysis, MARS presented the highest coefficient of determination (R2) values of (0.9998) and (0.9996) for training and testing respectively, with low MAD, MAPE and RMSE values. Comparatively, MARS outperformed RF in the predictions of pHRR, through its model algorithms that generated optimized equations for pHRR predictions, covering all non-linearity points of the experimental data. Amongst the input variables (sample mass, THR, HRC, pTemp and pTime), heating rate (β), highly influenced pHRR outcome predictions from MARS and RF models. However, to validate the performance and applicability of the proposed models. Results of MARS and RF were benchmarked with that from Artificial Neural Network (ANN) methods. The MARS and RF models observed the least error deviation when compared with pHRR results for PMMA from the ANN models. This study therefore, recommends the adoption of MARS and RF in the predictions of flammability characteristics of polymeric materials.

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