Predicting tunnel convergence using Multivariate Adaptive Regression Spline and Artificial Neural Network

The Indonesian Composite Stock Price Index is an indicator of changes in stock prices are a guide for investors to invest in reducing risk. Fluctuations in stock data tend to violate the assumptions of normality, homoscedasticity, autocorrelation, and multicollinearity. This problem can be overcome by modelling the Composite Stock Price Index uses an artificial neural network (ANN) and multivariate adaptive regression spline (MARS). In this study, the time-series data from the Composite Stock Price Index starting in April 2003 to March 2018 with its predictor variables are crude oil prices, interest rates, inflation, exchange rates, gold prices, Down Jones, and Nikkei 225. Based on the coefficient of determination, the determination coefficient of ANN is 0.98925, and the MARS determination coefficient is 0.99427. While based on the MAPE value, MAPE value of ANN was obtained, namely 6.16383 and MAPE value of MARS, which was 4.51372. This means that the ANN method and the good MARS method are used to forecast the value of the Indonesian Composite Stock Index in the future, but the MARS method shows the accuracy of the model is slightly better than ANN.

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Performance Evaluation for Mean Sea Level Prediction using Multivariate Adaptive Regression Spline and Artificial Neural Network

Ghana Mining Journal ◽

10.4314/gm.v18i1.1 ◽

2018 ◽

Vol 18 (1) ◽

pp. 1-8

Author(s):

Cynthia Borkai Boye ◽

Valentine Ikechukwu Amoah

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sea Level ◽

Multivariate Adaptive Regression Spline ◽

Ann Model ◽

Mean Sea Level ◽

Regression Spline ◽

Mars Model ◽

Adaptive Regression ◽

Artificial Neural

Mean sea level (MSL) has been used as a vertical datum for geodetic levelling and mapping in most countries all over the world. This is because the MSL approximates the geoid and serves as a realist reference surface that could be determined mostly through tide measurements over a period of time. However, sea levels have been rising over the years due to global warming and its associated climate change which continuous to melt ice sheets around the Polar Regions. This phenomenon is likely to affect the reliability of MSL, thus it is important to determine the local MSL at regular time periods. This study assessed the performance of Artificial Neural Network (ANN) and Multivariate Adaptive Regression Spline (MARS) models in predicting the MSL. Tide gauge records from the Takoradi Harbour of Ghana were used in the study. Monthly maximum, minimum and mean tidal values were derived from the secondary data and used for both model formulation and model testing. A comparative analysis of both models showed that the ANN model performed better than the MARS model. A Root Mean Square Error (RMSE) of 0.0359 m was obtained for the ANN model, whereas 0.0555 m was obtained for the MARS model. Mean Absolute Percentage Error (MAPE) of 3.1414% was obtained for the ANN model and whereas the MARS model yielded 5.6349%. A Mean Absolute Error (MAE) for the ANN model was 0.0284 m as against 0.0446 m for the MARS model. Correlation coefficient values of 0.9720 and 0.8874 were obtained for the ANN model and the MARS model respectively. An optimum ANN structure was found to be ANN 2-11-1. Based on the outcome of this study, it is recommended that ANN model should be adopted for forecasting local mean sea level for the study area. Keywords: Mean Sea Level, Artificial Neural Network, Multivariate Adaptive Regression Spline

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Prediction of Heavy Metals Concentration in the Groundwater Resources in Razan Plain: Extreme Learning Machine vs. Artificial Neural Network and Multivariate Adaptive Regression Spline

Annals of Military and Health Sciences Research ◽

10.5812/amh.98554 ◽

2019 ◽

Vol 17 (4) ◽

Author(s):

Meysam Alizamir ◽

Soheil Sobhanardakani ◽

Amin Hasanalipour Shahrabadi

Keyword(s):

Neural Network ◽

Heavy Metals ◽

Artificial Neural Network ◽

Extreme Learning Machine ◽

Groundwater Resources ◽

Multivariate Adaptive Regression Spline ◽

Regression Spline ◽

Adaptive Regression ◽

Artificial Neural ◽

Learning Machine

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Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models

Environmental and Engineering Geoscience ◽

10.2113/eeg-2243 ◽

2020 ◽

Vol 26 (2) ◽

pp. 185-200

Author(s):

Said Benchelha ◽

Hasnaa Chennaoui Aoudjehane ◽

Mustapha Hakdaoui ◽

Rachid El Hamdouni ◽

Hamou Mansouri ◽

...

Keyword(s):

Logistic Regression ◽

Landslide Susceptibility ◽

Vegetation Index ◽

Multivariate Adaptive Regression Spline ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Neural Network Models ◽

Regression Spline ◽

Adaptive Regression ◽

Artificial Neural

ABSTRACT Landslide susceptibility indices were calculated and landslide susceptibility maps were generated for the Oudka, Morocco, study area using a geographic information system. The spatial database included current landslide location, topography, soil, hydrology, and lithology, and the eight factors related to landslides (elevation, slope, aspect, distance to streams, distance to roads, distance to faults, lithology, and Normalized Difference Vegetation Index [NDVI]) were calculated or extracted. Logistic regression (LR), multivariate adaptive regression spline (MARSpline), and Artificial Neural Networks (ANN) were the methods used in this study to generate landslide susceptibility indices. Before the calculation, the study area was randomly divided into two parts, the first for the establishment of the model and the second for its validation. The results of the landslide susceptibility analysis were verified using success and prediction rates. The MARSpline model gave a higher success rate (AUC (Area Under The Curve) = 0.963) and prediction rate (AUC = 0.951) than the LR model (AUC = 0.918 and AUC = 0.901) and the ANN model (AUC = 0.886 and AUC = 0.877). These results indicate that the MARSpline model is the best model for determining landslide susceptibility in the study area.

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The Hybrid Logistics Regression-Artificial Neural Network and Multivariate Adaptive Regression Splines-Artificial Neural Network Modeling Schemes for Heart Disease Classification

Advanced Science Letters ◽

10.1166/asl.2013.5166 ◽

2013 ◽

Vol 19 (11) ◽

pp. 3405-3408 ◽

Cited By ~ 1

Author(s):

Yuehjen E. Shao ◽

Chia-Ding Hou ◽

Ya-Chi Chan ◽

Hung-Tzu Lin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Disease Classification ◽

Multivariate Adaptive Regression Splines ◽

Neural Network Modeling ◽

Regression Splines ◽

Logistics Regression ◽

Adaptive Regression ◽

Artificial Neural ◽

Adaptive Regression Splines

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Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data

Journal of Hydroinformatics ◽

10.2166/hydro.2011.044 ◽

2011 ◽

Vol 14 (3) ◽

pp. 731-744 ◽

Cited By ~ 94

Author(s):

Jan Adamowski ◽

Hiu Fung Chan ◽

Shiv O. Prasher ◽

Vishwa Nath Sharda

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wavelet Transform ◽

Multivariate Adaptive Regression Splines ◽

Ann Model ◽

Limited Data ◽

Runoff Forecasting ◽

Adaptive Regression ◽

Artificial Neural ◽

Mountainous Watersheds

Himalayan watersheds are characterized by mountainous topography and a lack of available data. Due to the complexity of rainfall–runoff relationships in mountainous watersheds and the lack of hydrological data in many of these watersheds, process-based models have limited applicability for runoff forecasting in these areas. In light of this, accurate forecasting methods that do not necessitate extensive data sets are required for runoff forecasting in mountainous watersheds. In this study, multivariate adaptive regression spline (MARS), wavelet transform artificial neural network (WA-ANN), and regular artificial neural network (ANN) models were developed and compared for runoff forecasting applications in the mountainous watershed of Sainji in the Himalayas, an area with limited data for runoff forecasting. To develop and test the models, three micro-watersheds were gauged in the Sainji watershed in Uttaranchal State in India and data were recorded from July 1 2001 to June 30 2003. It was determined that the best WA-ANN and MARS models were comparable in terms of forecasting accuracy, with both providing very accurate runoff forecasts compared to the best ANN model. The results indicate that the WA-ANN and MARS methods are promising new methods of short-term runoff forecasting in mountainous watersheds with limited data, and warrant additional study.

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Evaluation of Multivariate Adaptive Regression Splines and Artificial Neural Network for Prediction of Mean Sea Level Trend around Northern Australian Coastlines

Mathematics ◽

10.3390/math9212696 ◽

2021 ◽

Vol 9 (21) ◽

pp. 2696

Author(s):

Nawin Raj ◽

Zahra Gharineiat

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sea Level ◽

Multivariate Adaptive Regression Splines ◽

Regression Splines ◽

Ann Model ◽

Mean Sea Level ◽

Adaptive Regression ◽

Artificial Neural ◽

Adaptive Regression Splines

Mean sea level rise is a significant emerging risk from climate change. This research paper is based on the use of artificial intelligence models to assess and predict the trend on mean sea level around northern Australian coastlines. The study uses sea-level times series from four sites (Broom, Darwin, Cape Ferguson, Rosslyn Bay) to make the prediction. Multivariate adaptive regression splines (MARS) and artificial neural network (ANN) algorithms have been implemented to build the prediction model. Both models show high accuracy (R2 > 0.98) and low error values (RMSE < 27%) overall. The ANN model showed slightly better performance compared to MARS over the selected sites. The ANN performance was further assessed for modelling storm surges associated with cyclones. The model reproduced the surge profile with the maximum correlation coefficients ~0.99 and minimum RMS errors ~4 cm at selected validating sites. In addition, the ANN model predicted the maximum surge at Rosslyn Bay for cyclone Marcia to within 2 cm of the measured peak and the maximum surge at Broome for cyclone Narelle to within 7 cm of the measured peak. The results are comparable with a MARS model previously used in this region; however, the ANN shows better agreement with the measured peak and arrival time, although it suffers from slightly higher predictions than the observed sea level by tide gauge station.

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USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w5-31-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 31-36 ◽

Cited By ~ 6

Author(s):

M. Ahmadlou ◽

M. R. Delavar ◽

A. Tayyebi ◽

H. Shafizadeh-Moghadam

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Urban Areas ◽

Parametric Model ◽

Parametric Models ◽

Model Structure ◽

Regression Spline ◽

Adaptive Regression ◽

Modelling Process ◽

Non Parametric

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM<sup>+</sup>) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

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