scholarly journals Soil depth modelling using terrain analysis and satellite imagery: the case study of Qeshlaq mountainous watershed (Kurdistan, Iran)

2017 ◽  
Vol 48 (3) ◽  
pp. 167 ◽  
Author(s):  
Salahudin Zahedi ◽  
Kaka Shahedi ◽  
Mahmod Habibnejad Rawshan ◽  
Karim Solimani ◽  
Kourosh Dadkhah
Keyword(s):  
Soil Depth ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Terrain Analysis ◽  
Ann Model ◽  
Deep Soil ◽  
Explanatory Variables ◽  
Model Soil ◽  
Mountainous Watershed ◽  
Artificial Neural Network Ann

Soil depth is a major soil characteristic, which is commonly used in distributed hydrological modelling in order to present watershed subsurface attributes. This study aims at developing a statistical model for predicting the spatial pattern of soil depth over the mountainous watershed from environmental variables derived from a digital elevation model (DEM) and remote sensing data. Among the explanatory variables used in the models, seven are derived from a 10 m resolution DEM, namely specific catchment area, wetness index, aspect, slope, plan curvature, elevation and sediment transport index. Three variables landuse, NDVI and pca1 are derived from Landsat8 imagery, and are used for predicting soil depth by the models. Soil attributes, soil moisture, topographic curvature, training samples for each landuse and major vegetation types are considered at 429 profiles within four subwatersheds. Random forests (RF), support vector machine (SVM) and artificial neural network (ANN) are used to predict soil depth using the explanatory variables. The models are run using 336 data points in the calibration dataset with all 31 explanatory variables, and soil depth as the response of the models. Mean decrease permutation accuracy is performed on Variable selection. Testing dataset is done with the model soil depth values at testing locations (93 points) using different efficiency criteria. Prediction error is computed for both the calibration and testing datasets. Results show that the variables landuse, specific surface area, slope, pca1, NDVI and aspect are the most important explanatory variables in predicting soil depth. RF and SVM models are appropriate for the mountainous watershed areas that have been limited in the depth of the soil and ANN model is more suitable for watershed with the fields of agricultural and deep soil depth.

scholarly journals Simulating the Stress-Strain Relationship of Geomaterials by Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbo Zhao ◽  
Zenghui Huang ◽  
Zhengsheng Zou
Keyword(s):  
Support Vector Machine ◽  
Constitutive Relationship ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Ann Model ◽  
Stress Strain ◽  
Svm Model ◽  
Strain Relationship ◽  
Artificial Neural Network Ann ◽  
Relationship Of

Stress-strain relationship of geomaterials is important to numerical analysis in geotechnical engineering. It is difficult to be represented by conventional constitutive model accurately. Artificial neural network (ANN) has been proposed as a more effective approach to represent this complex and nonlinear relationship, but ANN itself still has some limitations that restrict the applicability of the method. In this paper, an alternative method, support vector machine (SVM), is proposed to simulate this type of complex constitutive relationship. The SVM model can overcome the limitations of ANN model while still processing the advantages over the traditional model. The application examples show that it is an effective and accurate modeling approach for stress-strain relationship representation for geomaterials.

Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District

2019 ◽  
Vol 28 (1) ◽  
pp. 35 ◽  
Author(s):  
Pablo Pozzobon de Bem ◽  
Osmar Abílio de Carvalho Júnior ◽  
Eraldo Aparecido Trondoli Matricardi ◽  
Renato Fontes Guimarães ◽  
Roberto Arnaldo Trancoso Gomes
Keyword(s):  
Logistic Regression ◽  
Characteristic Curve ◽  
Federal District ◽  
Burned Area ◽  
Ann Model ◽  
Wildfire Management ◽  
Explanatory Variables ◽  
Burned Areas ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.

Prediction of the optimal FSW process parameters for joints using machine learning techniques

2021 ◽  
Vol 63 (12) ◽  
pp. 1104-1111
Author(s):  
Furkan Sarsilmaz ◽  
Gürkan Kavuran
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Friction Stir ◽  
Learning Techniques ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Fsw Parameters

Abstract In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.

scholarly journals Use of GA-ANN and GA-SVM for a QSPR study on aqueous solubility of pesticides

2020 ◽  
pp. 66-66
Author(s):  
Amel Bouakkadia ◽  
Noureddine Kertiou ◽  
Rana Amiri ◽  
Youssouf Driouche ◽  
Djelloul Messadi
Keyword(s):  
Pearson Correlation ◽  
Chemical Properties ◽  
Aqueous Solubility ◽  
Important Variable ◽  
Support Vector ◽  
Ann Model ◽  
Experimental Procedure ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Selection Of

The partitioning tendency of pesticides, in these study herbicides in particular, into different environmental compartments depends mainly of the physic-chemical properties of the pesticides itself. Aqueous solubility (S) indicates the tendency of a pesticide to be removed from soil by runoff or irrigation and to reach surface water. The experimental procedure determining aqueous solubility of pesticides is very expensive and difficult. QSPR methods are often used to estimate the aqueous solubility of herbicides. The artificial neural network (ANN) and support vector machine (SVM) methods, every time associated with genetic algorithm (GA) selection of the most important variable, were used to develop QSPR models to predict the aqueous solubility of a series 80 herbicides. The values of log S of the studied compounds were well correlated with de descriptors. Considering the pertinent descriptors, a Pearson Correlation Squared (R2) coefficient of 0.8 was obtained for the ANN model with a structure of 5-3-1 and 0.8 was obtained for the SVM model using the RBF function for the optimal parameters values: C = 11.12; ? = 0.1111 and ? = 0.222.

scholarly journals Material Classification via Machine Learning Techniques: Construction Projects Progress Monitoring

2021 ◽  
Author(s):  
Wesam Salah Alaloul ◽  
Abdul Hannan Qureshi
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Industrial Revolution ◽  
Progress Monitoring ◽  
Concrete Block ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Ann Model ◽  
Material Classification ◽  
Artificial Neural Network Ann

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a python-based ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the readers, along with the resources and open-source web links.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

scholarly journals Dielectric technique combined with artificial neural network and support vector regression in moisture content prediction of olive

2020 ◽  
Vol 66 (No. 1) ◽  
pp. 1-7
Author(s):  
Mahdi Rashvand ◽  
Mahmoud Soltani Firouz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Support Vector Regression ◽  
Prediction Models ◽  
Electronic Device ◽  
Support Vector ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Olives are one of the most important agriculture crops in the world, which are harvested in different stages of growth for various uses. One of the ways to detect the adequate time to process the olives is to determine their moisture content. In this study, to determine the moisture content of olives, a dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. The dielectric properties of the olive fruits were measured using an electronic device in the range of 0.1–30 MHz. Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384–12–1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated the acceptable accuracy of the dielectric technique combined with the ANN model.

A Novel SVM-Based Method for Seismic First-Arrival Detecting

2010 ◽  
Vol 29-32 ◽  
pp. 973-978 ◽  
Author(s):  
Ming Chen ◽  
Yong Li ◽  
Jun Xie
Keyword(s):  
Support Vector ◽  
The Novel ◽  
Ann Model ◽  
Seismic Record ◽  
Feature Vectors ◽  
Svm Model ◽  
First Arrival ◽  
Ann Models ◽  
Novel Method ◽  
Artificial Neural Network Ann

First arrivals detecting on seismic record is important at all times. A novel support vector machine (SVM)-based method for seismic first-arrival pickup is proposed in this research. Firstly, the multi-resolution wavelet decomposition is used to de-noise the seismic record. And then, feature vectors are extracted from the denoise data. Finally, both SVM and artificial neural network (ANN) models are employed to train and predict the feature vectors. Experimental results demonstrate that the SVM model gives better accuracy than the ANN model. It is promising that the novel method is very prospective.

scholarly journals Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke using Brainstem Auditory Evoked Potential

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 673
Author(s):  
Jangjay Sohn ◽  
Il-Young Jung ◽  
Yunseo Ku ◽  
Yeongwook Kim
Keyword(s):  
Ischemic Stroke ◽  
Regression Models ◽  
Evoked Potential ◽  
Auditory Evoked Potential ◽  
Support Vector ◽  
Ann Model ◽  
Brainstem Auditory Evoked Potential ◽  
Prognosis Prediction ◽  
Nonlinear Regression Models ◽  
Artificial Neural Network Ann

To evaluate the feasibility of brainstem auditory evoked potential (BAEP) for rehabilitation prognosis prediction in patients with ischemic stroke, 181 patients were tested using the Korean version of the modified Barthel index (K-MBI) at admission (basal K-MBI) and discharge (follow-up K-MBI). The BAEP measurements were performed within two weeks of admission on average. The criterion between favorable and unfavorable outcomes was defined as a K-MBI score of 75 at discharge, which was the boundary between moderate and mild dependence in daily living activities. The changes in the K-MBI scores (discharge–admission) were analyzed by nonlinear regression models, including the artificial neural network (ANN) and support vector machine (SVM), with the basal K-MBI score, age, and interpeak latencies (IPLs) of the BAEP (waves I, I–III, and III–V). When including the BAEP features, the correlations of the ANN and SVM regression models increased to 0.70 and 0.64, respectively. In the outcome prediction, the ANN model with the basal K-MBI score, age, and BAEP IPLs exhibited a sensitivity of 92% and specificity of 90%. Our results suggest that the BAEP IPLs used with the basal K-MBI score and age can play an adjunctive role in the prediction of patient rehabilitation prognoses.

scholarly journals Prediction of mean wave overtopping at simple sloped breakwaters using kernel-based methods

2021 ◽  
Author(s):  
Shabnam Hosseinzadeh ◽  
Amir Etemad-Shahidi ◽  
Ali Koosheh
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Gaussian Process Regression ◽  
Structural Features ◽  
Support Vector ◽  
Ann Model ◽  
Wave Overtopping ◽  
Taylor Diagram ◽  
The Mean ◽  
Artificial Neural Network Ann

Abstract The accurate prediction of the mean wave overtopping rate at breakwaters is vital to have a safe design. Hence, providing a robust tool as a preliminary estimator can be useful for practitioners. Recently, soft computing tools such as artificial neural network (ANN) have been developed as alternatives to traditional overtopping formulae. The goal of this paper is to assess the capabilities of two kernel-based methods namely Gaussian process regression (GPR) and support vector regression for the prediction of mean wave overtopping rate at sloped breakwaters. An extensive dataset taken from EurOtop (2018) database, including rubble mound structures with permeable core, straight slopes, without berm, and crown wall, was employed to develop the models. Different combinations of the important dimensionless parameters representing structural features and wave conditions were tested based on the sensitivity analysis for developing the models. The obtained results were compared with those of the ANN model and the existing empirical formulae. The modified Taylor diagram was used to compare the models graphically. The results showed the superiority of kernel-based models, especially the GPR model over the ANN model and empirical formulae. In addition, the optimal input combination was introduced based on accuracy and the number of input parameters criteria. Finally, the physical consistencies of developed models were investigated the results, of which demonstrated the reliability of kernel-based models in terms of delivering physics of overtopping phenomenon.

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