scholarly journals Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

2020 ◽  
Vol 12 (5) ◽  
pp. 2022 ◽  
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Organic Content ◽  
Rank Test ◽  
Support Vector ◽  
Inference System ◽  
Erosion Rates ◽  
Northern Taiwan

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

scholarly journals Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hai-Bang Ly ◽  
Thuy-Anh Nguyen ◽  
Binh Thai Pham
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Soil Properties ◽  
Clay Content ◽  
Absolute Error ◽  
Experimental Methods ◽  
Liquid Limit ◽  
Support Vector ◽  
Data Set ◽  
Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

Estimating the Total Organic Carbon for Unconventional Shale Resources During the Drilling Process: A Machine Learning Approach

2021 ◽  
pp. 1-26
Author(s):  
Ahmed Mahmoud ◽  
Hany Gamal ◽  
Salaheldin Elkatatny ◽  
Ahmed Alsaihati
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Total Organic Carbon ◽  
Gamma Ray ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Drilling Process ◽  
Validation Data ◽  
Inference System ◽  
Data Points

Abstract Total organic carbon (TOC) is an essential parameter that indicates the quality of unconventional reservoirs. In this study, four machine learning (ML) algorithms of the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), functional neural networks (FNN), and random forests (RF) were optimized to evaluate the TOC. The novelty of this work is that the optimized models predict the TOC from the bulk gamma-ray (GR) and spectral GR logs of uranium, thorium, and potassium only. The ML algorithms were trained on 749 datasets from Well-1, tested on 226 datasets from Well-2, and validated on 73 data points from Well-3. The predictability of the optimized algorithms was also compared with the available equations. The results of this study indicated that the optimized ANFIS, SVR, and RF models overperformed the available empirical equations in predicting the TOC. For validation data of Well-3, the optimized ANFIS, SVR, and RF algorithms predicted the TOC with AAPE's of 10.6%, 12.0%, and 8.9%, respectively, compared with the AAPE of 21.1% when the FNN model was used. While for the same data, the TOC was assessed with AAPE's of 48.6%, 24.6%, 20.2%, and 17.8% when Schmoker model, ΔlogR method, Zhao et al. correlation, and Mahmoud et al. correlation was used, respectively. The optimized models could be applied to estimate the TOC during the drilling process if the drillstring is provided with GR and spectral GR logging tools.

scholarly journals Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt

2019 ◽  
Vol 9 (15) ◽  
pp. 3172 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Thanh-Hai Le ◽  
Cao-Thang Pham ◽  
Tien-Thinh Le ◽  
Lanh Si Ho ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Support Vector Machine ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Support Vector ◽  
Inference System ◽  
Stone Matrix Asphalt ◽  
Hybrid Artificial Intelligence ◽  
Stone Matrix

The main objective of this study is to develop and compare hybrid Artificial Intelligence (AI) approaches, namely Adaptive Network-based Fuzzy Inference System (ANFIS) optimized by Genetic Algorithm (GAANFIS) and Particle Swarm Optimization (PSOANFIS) and Support Vector Machine (SVM) for predicting the Marshall Stability (MS) of Stone Matrix Asphalt (SMA) materials. Other important properties of the SMA, namely Marshall Flow (MF) and Marshall Quotient (MQ) were also predicted using the best model found. With that goal, the SMA samples were fabricated in a local laboratory and used to generate datasets for the modeling. The considered input parameters were coarse and fine aggregates, bitumen content and cellulose. The predicted targets were Marshall Parameters such as MS, MF and MQ. Models performance assessment was evaluated thanks to criteria such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R). A Monte Carlo approach with 1000 simulations was used to deduce the statistical results to assess the performance of the three proposed AI models. The results showed that the SVM is the best predictor regarding the converged statistical criteria and probability density functions of RMSE, MAE and R. The results of this study represent a contribution towards the selection of a suitable AI approach to quickly and accurately determine the Marshall Parameters of SMA mixtures.

Prediction of drug synergy in cancer using ensemble-based machine learning techniques

2018 ◽  
Vol 32 (11) ◽  
pp. 1850132 ◽  
Author(s):  
Harpreet Singh ◽  
Prashant Singh Rana ◽  
Urvinder Singh
Keyword(s):  
Machine Learning ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Prediction Errors ◽  
Learning Approaches ◽  
Inference System ◽  
Drug Synergy ◽  
Learning Techniques

Drug synergy prediction plays a significant role in the medical field for inhibiting specific cancer agents. It can be developed as a pre-processing tool for therapeutic successes. Examination of different drug–drug interaction can be done by drug synergy score. It needs efficient regression-based machine learning approaches to minimize the prediction errors. Numerous machine learning techniques such as neural networks, support vector machines, random forests, LASSO, Elastic Nets, etc., have been used in the past to realize requirement as mentioned above. However, these techniques individually do not provide significant accuracy in drug synergy score. Therefore, the primary objective of this paper is to design a neuro-fuzzy-based ensembling approach. To achieve this, nine well-known machine learning techniques have been implemented by considering the drug synergy data. Based on the accuracy of each model, four techniques with high accuracy are selected to develop ensemble-based machine learning model. These models are Random forest, Fuzzy Rules Using Genetic Cooperative-Competitive Learning method (GFS.GCCL), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Dynamic Evolving Neural-Fuzzy Inference System method (DENFIS). Ensembling is achieved by evaluating the biased weighted aggregation (i.e. adding more weights to the model with a higher prediction score) of predicted data by selected models. The proposed and existing machine learning techniques have been evaluated on drug synergy score data. The comparative analysis reveals that the proposed method outperforms others in terms of accuracy, root mean square error and coefficient of correlation.

scholarly journals Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

2021 ◽  
Vol 21 (2) ◽  
pp. 1140
Author(s):  
Md Abul Kalam Azad ◽  
Anup Majumder ◽  
Jugal Krishna Das ◽  
Md Imdadul Islam
Keyword(s):  
Machine Learning ◽  
Signal Detection ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Energy Detection ◽  
Decision Time ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Inference System

<span>The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, the concept of weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), fuzzy <em>c</em>-means clustering (FCMC), support vector machine (SVM) and convolutional neural network (CNN) are applied in signal detection at FC taking signal-to-interference plus noise ratio of secondary users as parameter. The weighted Fuzzy rule gave the detection accuracy of 86.6%, which resembles the energy detection model of majority rule of FC; however, CNN gave an accuracy of 91.3% at the expense of more decision time. The FIS, FCMC and SVM gave some intermediate results; however, the combined method gave the best result compared to that of any individual technique.</span>

Sentiment Analysis

2018 ◽  
pp. 130-152 ◽  
Author(s):  
Syed Muzamil Basha ◽  
Dharmendra Singh Rajput
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Daily Activity ◽  
Opinion Mining ◽  
Fuzzy Inference ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Inference System ◽  
Neural Fuzzy

E-commerce has become a daily activity in human life. In it, the opinion and past experience related to particular product of others is playing a prominent role in selecting the product from the online market. In this chapter, the authors consider Tweets as a point of source to express users' emotions on particular subjects. This is scored with different sentiment scoring techniques. Since the patterns used in social media are relatively short, exact matches are uncommon, and taking advantage of partial matches allows one to significantly improve the accuracy of analysis on sentiments. The authors also focus on applying artificial neural fuzzy inference system (ANFIS) to train the model for better opinion mining. The scored sentiments are then classified using machine learning algorithms like support vector machine (SVM), decision tree, and naive Bayes.

Prediction of groundwater quality parameter in the Tabriz plain, Iran using soft computing methods

2019 ◽  
Vol 68 (7) ◽  
pp. 573-584 ◽  
Author(s):  
Robabeh Jafari ◽  
Ali Torabian ◽  
Mohammad Ali Ghorbani ◽  
Seyed Ahmad Mirbagheri ◽  
Amir Hessam Hassani
Keyword(s):  
Soft Computing ◽  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Support Vector ◽  
Eastern Region ◽  
Data Set ◽  
Inference System ◽  
Groundwater Aquifer ◽  
Tabriz Plain

Abstract Aquifers are one of the largest available freshwater resources. In this paper, total dissolved solids (TDS) of the groundwater aquifer in Tabriz plain is estimated by groundwater physicochemical parameters including Na, HCO3, Ca, Mg, and SO4 in the eastern region of Urmia Lake. For this purpose, four soft computing approaches, namely, multilayer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and gene expression programming (GEP) were used to predict TDS for a period of 10 years (2002–2012). Data were collected from the East Azerbaijan Regional Water Organization, which totaled 1,742 samples. In the application, of the whole data set, 70% (1,220 samples) was used for training and 30% (522 samples) for testing. In the following, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) statistics were used for evaluating the accuracy of the models. According to the results, MLP, ANFIS, SVM, and GEP models could be employed successfully in estimating TDS alterations. A comparison was made between these soft computing approaches that corroborated the superiority of the GEP model over MLP, SVM, and ANFIS models with RMSE = 58.93, R = 0.998, and MAE = 5.21.

scholarly journals Comparison of SVM, ANFIS and GEP in modeling monthly potential evapotranspiration in an arid region (Case study: Sistan and Baluchestan Province, Iran)

2018 ◽  
Vol 19 (2) ◽  
pp. 392-403 ◽  
Author(s):  
Omolbani Mohammadrezapour ◽  
Jamshid Piri ◽  
Ozgur Kisi
Keyword(s):  
Fuzzy Inference ◽  
Climatic Factors ◽  
Potential Evapotranspiration ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Heuristic Methods ◽  
Inference System

Abstract Evapotranspiration is an important component in planning and management of water resources. It depends on climatic factors and the influence of these factors on each other makes evapotranspiration estimation difficult. This study attempts to explore the possibility of predicting this important component using three different heuristic methods: support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). In this regard, according to the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith equation, the monthly potential evapotranspiration in four synoptic stations (Zahedan, Zabol, Iranshahr, and Chabahar) was calculated using monthly weather data. The weather data were then used as inputs to the SVM, ANFIS and GEP models to estimate potential evapotranspiration. Five different input combinations were tried in the applications. The results of SVM, ANFIS and GEP models were compared based on the coefficient of determination (R2), mean absolute error and root mean square error. Findings showed that the SVM model, whose inputs are average air temperature, relative humidity, wind speed, and sunny hours of the current and one previous month, performed better than the other models for the Zahedan, Zabol, Iranshahr, and Chabahar stations. Comparison of the three heuristic methods indicated that in all stations, the SVM, GEP and ANFIS models took first, second, and third place in estimation of the monthly potential evapotranspiration, respectively.

scholarly journals Machine Learning for Air Transport Planning and Management

2021 ◽  
Author(s):  
Graham Wild ◽  
Glenn Baxter ◽  
Panarat Srisaeng ◽  
Steven Richardson
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Traditional Approach ◽  
Demand Forecasting ◽  
Machine Learning Algorithms ◽  
Air Transport ◽  
Civil Aviation ◽  
Support Vector ◽  
Inference System

In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error.

scholarly journals Reference Evapotranspiration Modeling Using New Heuristic Methods

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 547 ◽  
Author(s):  
Rana Muhammad Adnan ◽  
Zhihuan Chen ◽  
Xiaohui Yuan ◽  
Ozgur Kisi ◽  
Ahmed El-Shafie ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Fuzzy Inference ◽  
Reference Evapotranspiration ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
M5 Model Tree ◽  
Inference System

The study investigates the potential of two new machine learning methods, least-square support vector regression with a gravitational search algorithm (LSSVR-GSA) and the dynamic evolving neural-fuzzy inference system (DENFIS), for modeling reference evapotranspiration (ETo) using limited data. The results of the new methods are compared with the M5 model tree (M5RT) approach. Previous values of temperature data and extraterrestrial radiation information obtained from three stations, in China, are used as inputs to the models. The estimation exactness of the models is measured by three statistics: root mean square error, mean absolute error, and determination coefficient. According to the results, the temperature or extraterrestrial radiation-based LSSVR-GSA models perform superiorly to the DENFIS and M5RT models in terms of estimating monthly ETo. However, in some cases, a slight difference was found between the LSSVR-GSA and DENFIS methods. The results indicate that better prediction accuracy may be obtained using only extraterrestrial radiation information for all three methods. The prediction accuracy of the models is not generally improved by including periodicity information in the inputs. Using optimum air temperature and extraterrestrial radiation inputs together generally does not increase the accuracy of the applied methods in the estimation of monthly ETo.

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