scholarly journals Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

2021 ◽  
Author(s):  
P. Velumani ◽  
N.V.N. Nampoothiri
Keyword(s):  
Mean Square Error ◽  
Power Plants ◽  
Consumer Price Index ◽  
Construction Cost ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Cost Index ◽  
Smoothing Techniques ◽  
Resource Data

AbstractThe Construction Industry Development Council (CIDC) of India has been calculating and publishing the Construction Cost Index (CCI), monthly, since 1998. Construction cost variations interrogate different kinds of projects such as roads, power plants, buildings, industrial structures, railways and bridges. The success rate of completion of construction project is diminished due to the lack of prediction knowledge in CCI. Predicting CCI in greater accuracy is quite difficult for contractor and academicians. The following factors are influenced higher in CCI such as population, unemployment rate, consumer price index (CPI), long term interest rate, domestic credit growth, Gross Domestic Product (GDP) and money supply (M4). CCI can be used to forecast the construction cost. The relevant resource data was collected across the nation between 2003 and 2018. As outcome-based, non-econometric tools such as smoothing techniques, artificial neural network (ANN) and support vector machines (SVMs) have produced a better outcome. Among these, smoothing techniques have given the notable low error and high accuracy. This accuracy has measured by Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The major objective of this research is to help the cost estimator to avoid underestimation and overestimation.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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%.

scholarly journals Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1341
Author(s):  
Yuju Ma ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Qiang Liu ◽  
Lulu Liu
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Human Activities ◽  
Vegetation Index ◽  
Terrestrial Ecosystem ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Response Factors

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.

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.

scholarly journals Chaos Time Series Prediction Based on Membrane Optimization Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Meng Li ◽  
Liangzhong Yi ◽  
Zheng Pei ◽  
Zhisheng Gao ◽  
Hong Peng
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Membrane Computing ◽  
Spectrum Management ◽  
Single Step ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Proposed Model

This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction(τ,m)and least squares support vector machine (LS-SVM)(γ,σ)by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM) broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

scholarly journals Estimating the wear rate of diamond cutting wire segment in building stone cutting using SVR and GA_MLP system

2021 ◽  
Author(s):  
Omid Jami Babajan ◽  
Raheb Bagherpour
Keyword(s):  
Wear Rate ◽  
Mean Square Error ◽  
Building Stone ◽  
Rock Properties ◽  
Polynomial Kernel ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Diamond Cutting ◽  
Wire Segment

Abstract As the cutting stone is a wear process, performing this process with diamond pieces' aid can be considered the wear of stone particles bypassing diamond grains on its surface. To better understand this process as well as the conditions governing the cutting diamond grain, it is necessary to familiar with the cutting mechanism along with the affecting parameters. In this matter, predicting the amount of segment consumption plays a prominent role to estimate the production cost as well as to schematize the building stone mines. This paper utilized the data obtained from Carbonate and Granite stones to estimate the amount of consumption of diamond cutting wire segments. To do so, two methods, namely support vector regression (SVR) and genetic algorithm + Multilayer perceptron (GA-MLP) were chosen using the MATLAB software toolboxes in order to estimate the segment erosion. In each of the above algorithms, a lowpass smoothing filter, called Savitzky-Golay was employed on the data. For this purpose, three rock properties including uniaxial compressive strength, Shimazk friction factor, and Young's modulus, were also employed as the model's input. After that, twelve models were constructed and then the segment erosion was estimated as well. Ultimately, the accuracy of the above models was assessed using the coefficient of determination (\({R}^{2}\)), mean square error (MSE), root mean square error (RMSE), mean value absolute error (MAD), mean absolute percentage error (MAPE), and variance of factor analysis (VFA). According to the obtained results, it can be concluded that the SVR approach and the Savitzky-Golay filter with Polynomial Kernel could better estimate the wear rate of the diamond cutting wire segment.

scholarly journals PEMODELAN JUMLAH PENDUDUK MISKIN DI JAWA TENGAH MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

2019 ◽  
Vol 4 (2) ◽  
pp. 10
Author(s):  
Sugi Haryanto ◽  
Gilang Axelline Andriani
Keyword(s):  
Sustainable Development ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Geographically Weighted Regression ◽  
Sustainable Development Goals ◽  
Weighted Regression ◽  
Percentage Error ◽  
Mean Square ◽  
Development Goals

Kemiskinan merupakan sesuatu yang sering menjadi ukuran keberhasilan kepemimpinan seorang kepala daerah. Selain itu juga sebagai tujuan pertama Sustainable Development Goals (SDG’s) untuk dientaskan. Kebijakan yang tepat sangat penting dibuat demi tercapainya tujuan pembangunan berkelanjutan. Pemodelan Geographically Weighted Regression (GWR) penting digunakan untuk menyusun model di setiap kabupaten/kota sebagai dasar pembuat kebijakan. Peubah yang digunakan dalam penelitian ini yaitu jumlah penduduk miskin, Indeks Pembangunan Manusia (IPM), Tingkat Pengangguran Terbuka (TPT), dan Upah Minimum Kabupaten/kota (UMK). Tujuan penelitian ini yaitu menentukan faktor-faktor yang berpengaruh terhadap jumlah penduduk miskin di setiap kabupaten/kota di Jawa Tengah. Pemodelan GWR lebih efektif dalam menggambarkan jumlah penduduk miskin di kabupaten/kota di Jawa Tengah tahun 2018. Hal ini ditunjukkan dengan adanya penigkatan nilai R2 serta penurunan nilai Root Mean Square Error (RMSE) dan Mean Absolute Percentage Error (MAPE).

scholarly journals FORECASTING INTERNATIONAL TOURIST ARRIVALS IN MALAYSIA USING SARIMA AND HOLT-WINTERS MODEL

2020 ◽  
Vol 5 (18) ◽  
pp. 41-51
Author(s):  
Norliana Mohd Lip ◽  
Nur Shafiqah Jumery ◽  
Fatin Amira Ahmad Termizi ◽  
Nurul Atiqa Mulyadi ◽  
Norhasnelly Anuar ◽  
...  
Keyword(s):  
United Kingdom ◽  
Mean Square Error ◽  
Time Series Data ◽  
Tourism Industry ◽  
Series Data ◽  
Percentage Error ◽  
Mean Square ◽  
Public And Private ◽  
The United Kingdom ◽  
Tourism Flows

Tourism can be described as the activities of visitors who make a visit to the main destination outside their usual environment for less than a year for any purpose. The tourism industry has become one of the influential sectors in global economic growth. Thus, tourism forecasting plays an important role in public and private sectors concerning future tourism flows. This study is an attempt to determine the best model in forecasting the international tourist's arrival in Malaysia based on Box-Jenkins and Holt-Winters model. The comparison of the accuracy of the techniques between Box-Jenkins SARIMA and Holt-Winters model was done based on the value of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The secondary time series data were obtained from the Tourism Malaysia Department, which consists of a number of tourist arrivals from Singapore, Korea, and the United Kingdom from the year 2013 until the year 2017. The findings of this study suggest that the SARIMA and Holt-Winters model are suitable to be used in forecasting tourist arrivals. This study found that the Holt-Winters model is the appropriate model to forecast tourist arrivals from the United Kingdom (UK) and Korea. While SARIMA (1,1,1) (1,1,1)12 is the appropriate model for forecasting tourist arrivals from Singapore.

scholarly journals BISAKAH MEMPEROLEH STATISTIK INDEKS HARGA KONSUMEN TINGKAT PROVINSI DI INDONESIA DENGAN KETELITIAN YANG LEBIH BAIK?

2020 ◽  
Vol 2019 (1) ◽  
pp. 297-306
Author(s):  
Andi Okta Fengki ◽  
Khairil Anwar Notodiputro ◽  
Kusman Sadik
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Small Area ◽  
Price Index ◽  
Small Area Estimation ◽  
Consumer Price Index ◽  
Mean Square ◽  
Area Estimation ◽  
Relative Root

Statistik indeks harga konsumen (IHK) atau consumer price index (CPI) juga dibutuhkan pada tingkat provinsi di era desentralisasi saat ini. Ketika IHK ingin diduga pada tingkat provinsi, permasalahan ukuran contoh kecil (small area) muncul karena survei untuk menghasilkan IHK ini di Indonesia dirancang untuk tingkat nasional. Akan tetapi, informasi dari statistik IHK 82 kota dapat membantu untuk menduga IHK provinsi. Metode pendugaan area kecil atau small area estimation (SAE) dapat diterapkan sebagai solusi untuk meningkatkan ketelitian hasil pendugaan langsung. Pada penelitian ini IHK provinsi diduga menggunakan model Fay-Herriot (FH). Hasilnya menunjukan bahwa model FH dapat menghasilkan statistik IHK provinsi dengan ketelitian yang lebih baik dari pendugaan langsung. Hal ini ditunjukan dengan nilai average relative root mean square error (ARRMSE) penduga FH IHK provinsi yang lebih kecil dari penduga langsungnya.

scholarly journals sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Fujun Ma ◽  
Fanghao Song ◽  
Yan Liu ◽  
Jiahui Niu
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Prediction Model ◽  
Mean Square Error ◽  
Short Term Memory ◽  
Experimental Results ◽  
Support Vector ◽  
Mean Square ◽  
Semg Signals ◽  
Research Studies

The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals. The existing research studies focus on the fatigue of independent gestures, while the research studies on integrated gestures are few. However, the actual gesture operation mode is usually integrated by multiple independent gestures, so the fatigue degree of integrated gestures can be predicted by training neural network of independent gestures. Three natural gestures including browsing information, playing games, and typing are divided into nine independent gestures in this paper, and the predicted model is established and trained by calculating the energy consumption of independent gestures. The artificial neural networks (ANNs) including backpropagation (BP) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) are used to predict the fatigue of gesture. The support vector machine (SVM) is used to assist verification. Mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are utilized to evaluate the optimal prediction model. Furthermore, the different datasets of the processed sEMG signal and its decomposed wavelet coefficients are trained, respectively, and the changes of error functions of them are compared. The experimental results show that LSTM model is more suitable for gesture fatigue prediction. The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture. It is better to use wavelet decomposition coefficients as datasets to predict the high-dimensional sEMG signals of two-handed gestures. The experimental results can be applied to predict the fatigue degree of complex human-machine interactive gestures, help to avoid unreasonable gestures, and improve the user’s interactive experience.

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