Multivariate Adaptive Regression Splines Technique Research Articles

Big Data Analytics with the Multivariate Adaptive Regression Splines to Analyze Key Factors Influencing Accident Severity in Industrial Zones of Thailand: A Study on Truck and Non-Truck Collisions

Machine learning currently holds a vital position in predicting collision severity. Identifying factors associated with heightened risks of injury and fatalities aids in enhancing road safety measures and management. Presently, Thailand faces considerable challenges with respect to road traffic accidents. These challenges are particularly acute in industrial zones, where they contribute to a rise in injuries and fatalities. The mixture of heavy traffic, comprising both trucks and non-trucks, significantly amplifies the risk of accidents. This situation, hence, generates profound concerns for road safety in Thailand. Consequently, discerning the factors that influence the severity of injuries and fatalities becomes pivotal for formulating effective road safety policies and measures. This study is specifically aimed at predicting the factors contributing to the severity of accidents involving truck and non-truck collisions in industrial zones. It considers a variety of aspects, including roadway characteristics, underlying assumptions of cause, crash characteristics, and weather conditions. Due to the fact that accident data is big data with specific characteristics and complexity, with the employment of machine learning in tandem with the Multi-variate Adaptive Regression Splines technique, we can make precise predictions to identify the factors influencing the severity of collision outcomes. The analysis demonstrates that various factors augment the severity of accidents involving trucks. These include darting in front of a vehicle, head-on collisions, and pedestrian collisions. Conversely, for non-truck related collisions, the significant factors that heighten severity are tailgating, running signs/signals, angle collisions, head-on collisions, overtaking collisions, pedestrian collisions, obstruction collisions, and collisions during overcast conditions. These findings illuminate the significant factors influencing the severity of accidents involving trucks and non-trucks. Such insights provide invaluable information for developing targeted road safety measures and policies, thereby contributing to the mitigation of injuries and fatalities.

Signal drift modeling and analysis of an intelligent instrument using a multivariate adaptive regression splines technique

Signal drift is an important failure mode of intelligent instruments under the combined effect of complex loading conditions and usually exhibits strongly nonlinear characteristics with a high degree of scatter. To solve this problem, this paper applies a multivariate adaptive regression splines (MARS) model to flexibly fit the nonlinear and highly inconsistent signal drifts of intelligent instruments. To investigate the pressure signal drift characteristics of different test transmitters, a unified parameter extraction method is proposed that uses a four-loop recursive algorithm to obtain consistent MARS model parameters. Through an application case study on five intelligent pressure transmitters tested at two temperatures, it is verified that the proposed unified parameter extraction method works perfectly for the fitting and analysis of pressure signal drift of these transmitters. Furthermore, it has advantages in providing quantitative interpretation of signal drift scatter, with potential practical application to investigation and comparison of drift fault curves of instruments that exhibit significant inconsistency.

Predictive explicit expressions from data-driven models for estimation of scour depth below ski-jump bucket spillways

Abstract Scour depth estimation is an essential factor in water-related engineering problems. Scouring below spillways may endanger a dam's stability and even lead to dam destruction. As a result, it has undesirable environmental effects due to dam failure. Hence, reliable and accurate scour depth estimation below spillways is an exciting topic for researchers. For this purpose, the published and reliable prototype data related to scour depth below ski jump bucket spillways (Ds) was used to develop data-driven models. This study employed two widely used decision tree (DT) methods, including the M5 model tree (M5MT) and the classification and regression tree (CART), and also multivariate adaptive regression splines (MARS) for the estimation of (Ds). The proposed methods provided explicit and clear equations with straightforward applications for estimating scour depth. For the quantitative assessments of the developed formulas, three common statistical metrics, namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC), were used. Moreover, comparison results with previous approaches existing in the literature indicated the efficacy of the suggested methods. The obtained results revealed that the MARS technique was the best approach for the estimation of scour depth.

A novel artificial intelligence approach for regolith geochemical grade prediction using multivariate adaptive regression splines

• Multivariate Adaptive Regression Spline (MARS) is proposed. • The proposed MARS approach was used to predict regolith geochemical grade. • Three other artificial intelligence techniques and kriging were compared with MARS. • The MARS technique gave superior results. The necessity for applying a potent analytical regolith geochemical grade estimator is driven by the reality of mineral exploration. This is because many exploration geologists rely upon the classical geostatistical method of Kriging which oftentimes do not produce accurate predictions due to the complexity of interactions between geological features and spatial variables. In this study, a novel non-linear data-driven approach known as Multivariate Adaptive Regression Spline (MARS) is proposed as an effective predictive tool to unravel regolith geochemical complexities. The proposed MARS approach was used to predict regolith geochemical grade from a thick regolith cover in the Tarkwaian paleo-placer of the South-Western Ashanti belt in Ghana. Out of the 891 samples, the data was partitioned into 70% training (model development) and 30% testing (model validation). The proposed MARS result was compared with three other artificial intelligence techniques (i.e., radial basis function neural network, backpropagation neural network and generalised regression neural network) and kriging geostatistical technique. Based on the test results, MARS had the highest correlation coefficient ( R = 0.9675) and the least statistical error metrics (RMSE = 0.7791, MAE = 0.6014, and ρ = 0.0472). The implementation of the MARS approach in regolith geochemical grade estimation domain has yielded outstanding and promising results. The MARS superiority was evident in its calibration strength, prediction accuracy, robust interaction of variables and overcoming the black box system of ANN. Thus, the proposed MARS approach could be an excellent tool in regolith geochemical grade estimation workflow when fully integrated with exploration tasks.

Prediction of Uplift Capacity of Cylindrical Caissons in Anisotropic and Inhomogeneous Clays Using Multivariate Adaptive Regression Splines

The uplift capacity factor of cylindrical suction caisson in anisotropic and inhomogeneous clays considering the adhesion factor at the interface is investigated in this paper. The finite element limit analysis based on lower bound and upper bound analyses is used for analyzing purposes. The anisotropic undrained shear model is employed to describe the anisotropic and inhomogeneous clay. The impact of these dimensionless parameters on the ratio of inhomogeneity or strength gradient ratio, the adhesion factor, the ratio of depth over diameter, and the ratio of anisotropic undrained shear strengths on the uplift resistance and the collapse mechanisms of suction caisson foundations are determined. The multivariate adaptive regression splines technique is employed to access the sensitivity of all considered dimensionless parameters on the uplift capacity factor and to propose an empirical design equation as an effective tool for predicting the uplift capacity factor. The results presented in this paper can be guidance for the preliminary design of suction caissons in anisotropic and non-homogeneous clays that are useful for engineering practitioners.

A Comparison between MLR, MARS, SVR and RF Techniques: Hydrological Time-series Modeling

Pan evaporation modeling is an essential part of water resources management and water budget governance. The study's objective was to examine the suitability of regression and tree-based techniques for estimating pan evaporation from climatic variables. Multiple linear regression (MLR), multivariate adaptive regression splines (MARS), support vector machine (SVM) and random forest (RF) techniques are employed for weekly pan evaporation modeling for the Ranichauri station situated in the Mid-Himalayan region of Uttarakhand, India. The determination of the most appropriate inputs among climatic variables to map evaporation was done by regression approaches. The data was divided into two parts: the first three years of data used for calibration and the remainder of the one-year data used for model validation. Statistical indices such as root mean square error (RMSE), Nash Sutcliffe coefficient of efficiency (NSE), and coefficient of determination (R2) were used to assess the performance of weekly pan evaporation estimating models. Based on scatter plots, the results are under-predicted and over-predicted for the weekly pan evaporation values. The results showed that the values of the RMSE values ranged from 0.542 to 0.689, the NSE values ranged from 0.953 to 0.974, and the highest R2value was found for the SVR model for the testing period. Therefore, the SVR model was found to be superior and can be applied to predict weekly pan evaporation values for the Ranichauri site. Doi: 10.28991/HEF-2022-03-01-07 Full Text: PDF

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.

Pipe Break Rate Assessment While Considering Physical and Operational Factors: A Methodology based on Global Positioning System and Data-Driven Techniques

An accurate prediction of pipes failure rate plays a substantial role in the management of Water Distribution Networks (WDNs). In this study, a field study was conducted to register pipes break and relevant causes in the WDN of Yazd City, Iran. In this way, 851 water pipes were incepted and localized by the Global Positioning System (GPS) apparatus. Then, 1033 failure cases were reported in the eight zones of understudy WDN during March-December 2014. Pipes break rate (BRP) was calculated using the depth of pipe installation (hP), number of failures (NP), the pressure of water pipes in operation (P), and age of pipe (AP). After completing a pipe break database, robust Artificial Intelligence models, namely Multivariate Adaptive Regression Spline (MARS), Gene-Expression Programming (GEP), and M5 Model Tree were employed to extract precise formulation for the pipes break rate estimation. Results of the proposed relationships demonstrated that the MARS model with Coefficient of Correlation (R) of 0.981 and Root Mean Square Error (RMSE) of 0.544 provided more satisfying efficiency than the M5 model (R = 0.888 and RMSE = 1.096). Furthermore, statistical results indicated that MARS and GEP models had comparatively at the same accuracy level. Explicit equations by Artificial Intelligence (AI) models were satisfactorily comparable with those obtained by literature review in terms of various conditions: physical, operational, and environmental factors and complexity of AI models. Through a probabilistic framework for the pipes break rate, the results of first-order reliability analysis indicated that the MARS technique had a highly satisfying performance when MARS-extracted-equation was assigned as a limit state function.

Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques

The dynamic properties of Municipal Solid Waste (MSW) are site-specific and need to be evaluated separately in different regions. The laboratory-based evaluation of MSW has difficulties such as an unpleasant aroma or degradability of MSW, making the testing procedure unfavorable. Moreover, these evaluations are time- and cost-intensive, which may also require trained personnel to conduct the tests. To address this concern, alternatively, the shear modulus of MSW can be estimated through some predictive models. In this study, the shear modulus was evaluated using 153 cyclic triaxial tests. For this purpose, the effects of various factors, including the shear strain (ShS), age of the MSW (Age), percentage of plastic (POP), confining pressure (CP), unit weight (UW), and loading frequency (F) on the shear modulus of MSW were evaluated. The data obtained through laboratory experiments was then employed to model the dynamic response of MSW using four different machine learning techniques including Artificial Neural Networks (ANN), Multivariate Adaptive Regression Splines (MARS), Multi-Gene Genetic Programming (MGGP), and M5 model Tree (M5Tree). A comparison of the performance of developed models indicated that the ANN model outperformed the other models. More specifically, for ANN, MARS, MGGP, and M5Tree models, the corresponding values of R-squared equal to 0.9897, 0.9640, 0.9617, and 0.8482 for the training dataset, while the values for the testing dataset for ANN, MARS, MGGP, and M5Tree are 0.9812, 0.9551, 0.9574, and 0.8745. Furthermore, although the developed models using MARS and MGGP techniques resulted in more errors compared to the ANN technique, they were found to produce reliable predictions. To further compare the performance and efficiency of the developed models and study the effects of each input variable on the output variable (i.e., shear modulus), model validity, parametric study, and sensitivity analysis were performed.

More reliable predictions of clear-water scour depth at pile groups by robust artificial intelligence techniques while preserving physical consistency

This paper presents the results of an investigation on scour around pile groups under steady currents using artificial intelligence (AI) models. Namely, EPR (Evolutionary Polynomial Regression), GEP (Gene-Expression Programming), MARS (Multivariate Adaptive Regression Spline), and M5MT (M5 Model Tree) approaches were used to develop nonlinear regression equations for estimating the maximum equilibrium clear-water scour depth. In total, 321 datasets were collected from various literature sources for different pile group configurations also including the gap between piles and pile groups non-aligned with the flow direction. Results through training and testing phases showed that the MARS technique with Index of Agreement (IOA) of 0.984, Root Mean Square Error (RMSE) of 0.483, and Mean Absolute Error (MAE) of 0.250 provides more accurate estimates of the scour depth (normalized by the pile diameter) than EPR (IOA = 0.976, RMSE = 0.579, and MAE = 0.195), GEP (IOA = 0.972, RMSE = 0.628, and MAE = 0.295), and M5MT (IOA = 0.965, RMSE = 0.704, and MAE = 0.259) models. Conversely, the most frequently used literature formulas demonstrate unconvincing efficiency when wide range experimental data are considered. The sensitivity analysis, in terms of Sobol’s index, revealed that the ratio U/Uc, between the approach flow velocity, U, and the flow velocity, Uc, at the inception of sediment motion, is the most influential parameter with Total Sobol Index (TSI) of 0.514 and an opposite trend of scour with the ratio m/n (TSI = 0.023), between the number, m, of piles inline with the flow and that, n, of piles normal to the flow, was found.

Development of a data-driven model for spatial and temporal shallow landslide probability of occurrence at catchment scale

A combined method was developed to forecast the spatial and the temporal probability of occurrence of rainfall-induced shallow landslides over large areas. The method also allowed to estimate the dynamic change of this probability during a rainfall event. The model, developed through a data-driven approach basing on Multivariate Adaptive Regression Splines technique, was based on a joint probability between the spatial probability of occurrence (susceptibility) and the temporal one. The former was estimated on the basis of geological, geomorphological, and hydrological predictors. The latter was assessed considering short-term cumulative rainfall, antecedent rainfall, soil hydrological conditions, expressed as soil saturation degree, and bedrock geology. The predictive capability of the methodology was tested for past triggering events of shallow landslides occurred in representative catchments of Oltrepò Pavese, in northern Italian Apennines. The method provided excellently to outstanding performance for both the really unstable hillslopes (area under ROC curve until 0.92, true positives until 98.8%, true negatives higher than 80%) and the identification of the triggering time (area under ROC curve of 0.98, true positives of 96.2%, true negatives of 94.6%). The developed methodology allowed us to obtain feasible results using satellite-based rainfall products and data acquired by field rain gauges. Advantages and weak points of the method, in comparison also with traditional approaches for the forecast of shallow landslides, were also provided.

Estimation of daily dissolved oxygen concentration for river water quality using conventional regression analysis, multivariate adaptive regression splines, and TreeNet techniques.

The aim of this study was to model the surface water quality of the Broad River Basin, South Carolina. The most suitable two monitoring stations numbered as USGS 02156500 (Near Carlisle) and USGS 02160991 (Near Jenkinsville) were selected for the reason that the river water temperature (WT), pH, and specific conductance (SC), as well as dissolved oxygen (DO) concentration, were simultaneously monitored and recorded at these sites. The monitoring period from September 2016 to August 2017 was taken into account for the modeling studies. The electrical conductivity (EC) values corresponding to the river SC values were calculated. First, the conventional regression analysis (CRA) was applied to three regression forms, i.e., linear, power, and exponential functions, to estimate the river DO concentration. Then, the multivariate adaptive regression splines (MARS) and TreeNet gradient boosting machine (TreeNet) techniques were employed. Three performance statistics, i.e., root means square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe coefficient of efficiency (NS), were used to compare the estimation capabilities of these techniques. The TreeNet technique, which was used for the first time in the modeling of DO concentration, had higher estimation success with the RMSE, MAE, and NS values of 0.182mg/L, 0.123mg/L, and 0.990, respectively, for the Carlisle station and 0.313mg/L, 0.233mg/L, and 0.965, respectively, for the Jenkinsville station in the training phase. The MARS technique, which had limited availability of its application in the modeling of DO concentration, had higher estimation success with the RMSE, MAE, and NS values of 0.240mg/L, 0.195mg/L, and 0.981, respectively, for the Carlisle station and 0.527mg/L, 0.432mg/L, and 0.980, respectively, for the Jenkinsville station in the testing phase. Considering the RMSE and MAE values being lower, as well as NS values being higher for the model having an input combination of WT, pH, and EC, the Carlisle station came into prominence. It was concluded that international researchers, who have engaged in the river water quality modeling studies, can favor the MARS and TreeNET techniques without any hesitation and estimate the river DO concentration successfully. The models developed for the Carlisle station were tested with the data sets for the monitoring period from September 2017 to August 2018 at the same station. Similarly, the models developed for the Jenkinsville station were tested with the data sets for the monitoring period from September 2017 to August 2018 at the same station. It was concluded that the models could estimate the river DO concentrations very close to in situ measurements at the same site but for the different monitoring periods, too. Furthermore, the models developed for the Carlisle station were tested with the data sets from the Jenkinsville station for the same monitoring period. Similarly, the models developed for the Jenkinsville station were tested with the data sets from the Carlisle station for the same monitoring period. It was also concluded that the developed models could estimate the river DO concentrations very close to in situ measurements at different monitoring sites but for the same monitoring period on the same river, too. It can be asserted that the models developed for any monitoring site on a river can be employed for another monitoring site on the same river, too, as in the case of the Broad River, South Carolina.

A new strategy to benchmark and evaluate building electricity usage using multiple data mining technologies

This study presents a new strategy using cluster analysis, multivariate adaptive regression splines and conditional inference trees to benchmark and evaluate building electricity usage. Different from the existing studies, cluster analysis was first used to group buildings based on their annual electricity usage patterns in order to improve the accuracy of the benchmarking result. Multivariate adaptive regression splines technique was then applied to capture the non-linear relationships (i.e. benchmarking models) between building electricity usage per square meter and explanatory factors with enhanced interpretability. Conditional inference trees technique was further used to evaluate the benchmarking result. The performance of this strategy was evaluated using two-year time-series electricity usage data of 20 university buildings. The results showed that the multivariate adaptive regression splines can effectively describe the complex non-linear relationships between building electricity usage per square meter and explanatory variables. The conditional inference trees can help identify conditions when buildings had higher electricity usage. The results obtained from this study can be further used to assist in building energy auditing, and fault detection and diagnosis.

Riprap incipient motion for overtopping flows with machine learning models

Abstract Riprap stones are frequently applied to protect rivers and channels against erosion processes. Many empirical equations have been proposed in the past to estimate the unit discharge at the failure circumstance of riprap layers. However, these equations lack general impact due to the limited range of experimental variables. To overcome these shortcomings, support vector machine (SVM), multivariate adaptive regression splines (MARS), and random forest (RF) techniques have been applied in this study to estimate the approach densimetric Froude number at the incipient motion of riprap stones. Riprap stone size, streambank slope, uniformity coefficient of riprap layer stone, specific density of stones, and thickness of riprap layer have been considered as controlling variables. Quantitative performances of the artificial intelligence (AI) models have been assessed by many statistical measures including: coefficient of correlation (R), root mean square error (RMSE), mean absolute error (MAE), and scatter index (SI). Statistical performance of AI models indicated that SVM model with radial basis function (RBF) kernel had better performance (SI = 0.37) than MARS (SI = 0.75) and RF (SI = 0.63) techniques. The proposed AI models performed better than existing empirical equations. From a parametric study the results demonstrated that the erosion-critical stone-referred Froude number (Fs,c) is mainly controlled by the streambank slope.

Feature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection

Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving the highest accuracy (97.44%) compared to other classifiers.

Evaluating Slope Deformation of Earth Dams Due to Earthquake Shaking Using MARS and GMDH Techniques

Assessing the behavior of earth dams under dynamic loads is one of the most significant problems with the design of such large structures. The purpose of this study is to provide new models for predicting dam dispersion in real earthquake conditions. In the first phase, 103 real cases of deformation in earth dams were collected and analyzed due to earthquakes that occurred over recent years. Using nonlinear and machine learning techniques, i.e., group method of data handling (GMDH) and multivariate adaptive regression splines (MARS), two models for prediction of the slope deformation in earth dams under the various types of earthquakes were applied and developed. The main parameters used in these simulation techniques were earthquake magnitude (Mw), fundamental period ratio (Td/Tp), yield acceleration ratio (ay/amax) as inputs and value of slope deformation (Dave) as output. Finally, in order to check the accuracy of the results of the new models, a comparison was made with the previous relations and models in seismic conditions for the slope deformation in earth dams. The results showed that the MARS model, which is able to provide a mathematical equation, has a better result than the GMDH model. These new models are recommended to be used for future analyses based on their flexible capabilities.

Hot Metal Temperature Forecasting at Steel Plant Using Multivariate Adaptive Regression Splines

Steelmaking has been experiencing continuous challenges and advances concerning process methods and control models. Integrated steelmaking begins with the hot metal, a crude liquid iron that is produced in the blast furnace (BF). The hot metal is then pre-treated and transferred to the basic lined oxygen furnace (BOF) for refining, experiencing a non-easily predictable temperature drop along the BF–BOF route. Hot metal temperature forecasting at the BOF is critical for the environment, productivity, and cost. An improved multivariate adaptive regression splines (MARS) model is proposed for hot metal temperature forecasting. Selected process variables and past temperature measurements are used as predictors. A moving window approach for the training dataset is chosen to avoid the need for periodic re-tuning of the model. There is no precedent for the application of MARS techniques to BOF steelmaking and a comparable temperature forecasting model of the BF–BOF interface has not been published yet. The model was trained, tested, and validated using a plant process dataset with 12,195 registers, covering one production year. The mean absolute error of predictions is 11.2 °C, which significantly improves those of previous modelling attempts. Moreover, model training and prediction are fast enough for a reliable on-line process control.

Pipe failure rate prediction in water distribution networks using multivariate adaptive regression splines and random forest techniques

ABSTRACTThis paper presents the results of a comparison between multivariate adaptive regression splines (MARS) and random forest (RF) techniques in pipe failure prediction in two water distribution networks. In this regard, pipe diameter, pipe length, pipe installation depth, pipe age and average hydraulic pressure are considered as input variables. Results show that the RF outperforms the MARS which is found as an accurate pipe failure rate predictor. The proposed models are further evaluated through dividing the data into three parts of lower, medium and higher pipe failure rate values. According to the equations produced by MARS technique, three variables of pipe diameter, pipe age and average hydraulic pressure are distinguished as the most effective variables in predicting pipe failure rate in the first case study. Four variables of pipe diameter, pipe length, pipe age and average hydraulic pressure are determined as the most effective variables in the second case study.

Prediction of Parameters which Affect Beach Nourishment Performance Using MARS, TLBO, and Conventional Regression Techniques

Artificial beach nourishment is one of the most important environmentally friendly coastal protection methods since it protects the aesthetic and recreational values of the beach and increases its protective properties. Therefore, the main aim of the current study is to assess the accuracy of multivariate adaptive regression splines (MARS) in predicting the parameters, namely sediment transport coefficients (K) and the diffusion rate (Ω), which affect beach nourishment performance. The performance of the MARS was determined by comparison of the models using exponential, linear, and power regression equations trained by conventional regression analyses (CRA) and the teaching-learning based optimization (TLBO) algorithm. In all models, two different input data obtained from the experimental study were used, one dimensional and one non-dimensional. The results presented that the MARS models gave lower error values than the CRA and TLBO models according to the root mean square error, mean absolute error, and scattering index criteria. When the models were evaluated, it was revealed that dimensional and non-dimensional models gave approximate results. We proved that the dimensional and non-dimensional MARS models can be used to estimate the (K) and (Ω) values.

Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico

Abstract: Remote sensors can be used as a robust and effective means of monitoring isolated or inaccessible forest sites. In the present study, the multivariate adaptive regression splines (MARS) technique was successfully applied to remotely sensed data collected by the Landsat-8 satellite to estimate mean diameter at breast height (R2 = 0.73), mean crown cover (R2 = 0.55), mean volume (R2 = 0.57) and total volume per plot (R2 = 0.41) in the forest monitoring sites. However, the spectral data yielded poor estimates of tree number per plot (R2 = 0.22), the mean height (R2 = 0.25) and the mean diameter at base (R2 = 0.38). Seven spectral bands (band 1 to band 7), six vegetation indexes and other derived parameters (NDVI, SAVI, LAI, FPAR. ALB and ASR) and eight terrain variables derived from the digital elevation model (elevation, slope, aspect, plan curvature, profile curvature, transformed aspect, terrain shape index and wetness index) were used as predictors in the fitted models. To prevent over-parameterization only some of the predictor variables considered were included in each model. The results indicate the MARS technique is potentially suitable for estimating dasometric variables from using spectral data obtained by the Landsat-8 OLI sensor.