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

Mathematics ◽  
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.

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

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

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

2011 ◽  
Vol 14 (3) ◽  
pp. 731-744 ◽  
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.

scholarly journals ESTIMATING STRENGTH OF RUBBERIZED CONCRETE USING EVOLUTIONARY MULTIVARIATE ADAPTIVE REGRESSION SPLINES

2016 ◽  
Vol 22 (5) ◽  
pp. 711-720 ◽  
Author(s):  
Min-Yuan CHENG ◽  
Minh-Tu CAO
Keyword(s):  
Neural Network ◽  
Concrete Strength ◽  
Back Propagation ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Rubberized Concrete ◽  
Comparison Results ◽  
Study Results ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

This study proposes an artificial intelligence (AI) model to predict the compressive strength and splitting tensile strength of rubberized concrete. This Evolutionary Multivariate Adaptive Regression Splines (EMARS) model is a hybrid of the Multivariate Adaptive Regression Splines (MARS) and Artificial Bee Colony (ABC) within which MARS addresses learning and curve fitting and ABC implements optimization to determine the fittest parameter settings with minimal prediction error. K-fold cross validation was utilized to compare EMARS performance against four other benchmark data mining techniques including MARS, Back-propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Genetic Programming (GP). Comparison results showed EMARS to be the best model for predicting rubberized concrete strength and study results demonstrated EMARS as a reliable tool for civil engineers in the concrete construction industry.

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