scholarly journals Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Manizhe Zarei ◽  
Omid Bozorg-Haddad ◽  
Sahar Baghban ◽  
Mohammad Delpasand ◽  
Erfan Goharian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flood Control ◽  
Time Lag ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Average Error ◽  
Support Vector ◽  
Streamflow Forecasts ◽  
Flood Damages ◽  
Artificial Neural Network Ann

AbstractWater is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

MACHINE LEARNING ALGORITHMS FOR IDENTIFICATION OF ABNORMAL GLOW CURVES AND ASSOCIATED ABNORMALITY IN CaSO4:DY-BASED PERSONNEL MONITORING DOSIMETERS

2020 ◽  
Vol 190 (3) ◽  
pp. 342-351
Author(s):  
Munir S Pathan ◽  
S M Pradhan ◽  
T Palani Selvam
Keyword(s):  
Machine Learning ◽  
Glow Curve ◽  
Good Accuracy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Computationally Efficient ◽  
Artificial Neural Network Ann ◽  
First Time

Abstract In the present study, machine learning (ML) methods for the identification of abnormal glow curves (GC) of CaSO4:Dy-based thermoluminescence dosimeters in individual monitoring are presented. The classifier algorithms, random forest (RF), artificial neural network (ANN) and support vector machine (SVM) are employed for identifying not only the abnormal glow curve but also the type of abnormality. For the first time, the simplest and computationally efficient algorithm based on RF is presented for GC classifications. About 4000 GCs are used for the training and validation of ML algorithms. The performance of all algorithms is compared by using various parameters. Results show a fairly good accuracy of 99.05% for the classification of GCs by RF algorithm. Whereas 96.7% and 96.1% accuracy is achieved using ANN and SVM, respectively. The RF-based classifier is recommended for GC classification as well as in assisting the fault determination of the TLD reader system.

scholarly journals Glass Classification based on Machine Learning Algorithms

2020 ◽  
Vol 9 (11) ◽  
pp. 139-142
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Amorphous Solid ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Svm Algorithm ◽  
Artificial Neural Network Ann ◽  
Logistic Regression Algorithm

Glass Industry is considered one of the most important industries in the world. The Glass is used everywhere, from water bottles to X-Ray and Gamma Rays protection. This is a non-crystalline, amorphous solid that is most often transparent. There are lots of uses of glass, and during investigation in a crime scene, the investigators need to know what is type of glass in a scene. To find out the type of glass, we will use the online dataset and machine learning to solve the above problem. We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm, Support Vector Machine (SVM) algorithm, Random Forest algorithm, and Logistic Regression algorithm. By comparing all the algorithm Random Forest did the best in glass classification.

scholarly journals A Survey on Predicting Advanced Liver Fibrosis Using Different Machine Learning Algorithms

2020 ◽  
pp. 177-183
Author(s):  
Krishnendu K B ◽  
Deepa S S
Keyword(s):  
Machine Learning ◽  
Liver Fibrosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Swarm Optimization ◽  
Advanced Liver Fibrosis ◽  
Forest Genetic ◽  
Multi Linear Regression ◽  
Artificial Neural Network Ann

Machine learning (ML) is a subsection of AI. The goal of ML is to understand the structure of data and fit that data into models that can be used for prediction, classification etc. Although machine learning is an area within computer science, it differs from traditional computational approaches. In recent years, different machine learning algorithms are used for disease prediction. Algorithms like Decision Tree (DT), Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Multi- Linear Regression, Random Forest, Genetic Algorithm (GA), Artificial Neural Network (ANN), Naive Bayes, etc. are used for classification. Using these algorithms liver fibrosis stages can be predicted. This paper discusses different machine learning algorithms for the prediction of liver fibrosis stage and the performance analysis of these algorithms in various studies.

scholarly journals Machine Learning Models for Spring Discharge Forecasting

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Francesco Granata ◽  
Michele Saroli ◽  
Giovanni de Marinis ◽  
Rudy Gargano
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Spring Discharge ◽  
Learning Models ◽  
Promising Alternative ◽  
Cross Correlation Analysis ◽  
Machine Learning Models

Nowadays, drought phenomena increasingly affect large areas of the globe; therefore, the need for a careful and rational management of water resources is becoming more pressing. Considering that most of the world’s unfrozen freshwater reserves are stored in aquifers, the capability of prediction of spring discharges is a crucial issue. An approach based on water balance is often extremely complicated or ineffective. A promising alternative is represented by data-driven approaches. Recently, many hydraulic engineering problems have been addressed by means of advanced models derived from artificial intelligence studies. Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was selected as a case study. The machine learning models have proven to be able to provide very encouraging results. M5P provides good short-term predictions of monthly average flow rates (e.g., in predicting average discharge of the spring after 1 month, R2=0.991, RAE=14.97%, if a 4-month input is considered), while RF is able to provide accurate medium-term forecasts (e.g., in forecasting average discharge of the spring after 3 months, R2=0.964, RAE=43.12%, if a 4-month input is considered). As the time of forecasting advances, the models generally provide less accurate predictions. Moreover, the effectiveness of the models significantly depends on the duration of the period considered for input data. This duration should be close to the aquifer response time, approximately estimated by cross-correlation analysis.

scholarly journals Machine Learning Model for Credit Card Fraud Detection- A Comparative Analysis

2021 ◽  
Author(s):  
Pratyush Sharma ◽  
Souradeep Banerjee ◽  
Devyanshi Tiwari ◽  
Jagdish Chandra Patni
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Model ◽  
Artificial Neural Network Ann

In today's world, we are on an express train to a cashless society which has led to a tremendous escalation in the use of credit card transactions. But the flipside of this is that fraudulent activities are on the increase; therefore, implementation of a methodical fraud detection system is indispensable to cardholders as well as the card-issuing banks. In this paper, we are going to use different machine learning algorithms like random forest, logistic regression, Support Vector Machine (SVM), and Neural Networks to train a machine learning model based on the given dataset and create a comparative study on the accuracy and different measures of the models being achieved using each of these algorithms. Using the comparative analysis on the F_1 score, we will be able to predict which algorithm is best suited to serve our purpose for the same. Our study concluded that Artificial Neural Network (ANN) performed best with an F_1 score of 0.91.

scholarly journals Predicting heart failure using a wrapper-based feature selection

2021 ◽  
Vol 21 (3) ◽  
pp. 1530
Author(s):  
Minh Tuan Le ◽  
Minh Thanh Vo ◽  
Nhat Tan Pham ◽  
Son V.T Dao
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Medical Practitioners ◽  
Machine Learning Model ◽  
Heart Contraction ◽  
Artificial Neural Network Ann

In the current health system, it is very difficult for medical practitioners/physicians to diagnose the effectiveness of heart contraction. In this research, we proposed a machine learning model to predict heart contraction using an artificial neural network (ANN). We also proposed a novel wrapper-based feature selection utilizing a grey wolf optimization (GWO) to reduce the number of required input attributes. In this work, we compared the results achieved using our method and several conventional machine learning algorithms approaches such as support vector machine, decision tree, K-nearest neighbor, naïve bayes, random forest, and logistic regression. Computational results show not only that much fewer features are needed, but also higher prediction accuracy can be achieved around 87%. This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.

scholarly journals Retrieval of Daily PM2.5 Concentrations Using Nonlinear Methods: A Case Study of the Beijing–Tianjin–Hebei Region, China

2018 ◽  
Vol 10 (12) ◽  
pp. 2006 ◽  
Author(s):  
Lijuan Li ◽  
Baozhang Chen ◽  
Yanhu Zhang ◽  
Youzheng Zhao ◽  
Yue Xian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fine Particulate Matter ◽  
Regression Tree ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Observation System ◽  
Support Vector ◽  
Observation Data ◽  
Earth Observing System ◽  
Moderate Resolution Imaging Spectroradiometer

Exposure to fine particulate matter (PM2.5) is associated with adverse health impacts on the population. Satellite observations and machine learning algorithms have been applied to improve the accuracy of the prediction of PM2.5 concentrations. In this study, we developed a PM2.5 retrieval approach using machine-learning methods, based on aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System (EOS) Terra and Aqua polar-orbiting satellites, near-ground meteorological variables from the NASA Goddard Earth Observing System (GEOS), and ground-based PM2.5 observation data. Four models, which are orthogonal regression (OR), regression tree (Rpart), random forests (RF), and support vector machine (SVM), were tested and compared in the Beijing–Tianjin–Hebei (BTH) region of China in 2015. Aerosol products derived from the Terra and Aqua satellite sensors were also compared. The 10-repeat 5-fold cross-validation (10 × 5 CV) method was subsequently used to evaluate the performance of the different aerosol products and the four models. The results show that the performance of the Aqua dataset was better than that of the Terra dataset, and that the RF algorithm has the best predictive performance (Terra: R = 0.77, RMSE = 43.51 μg/m3; Aqua: R = 0.85, RMSE = 33.90 μg/m3). This study shows promise for predicting the spatiotemporal distribution of PM2.5 using the RF model and Aqua aerosol product with the assistance of PM2.5 site data.

scholarly journals Machine Learning Models of Breast Cancer Risk Prediction

2019 ◽  
Author(s):  
Md. Mohaimenul Islam ◽  
Tahmina Narin Poly
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Features ◽  
Machine Learning Algorithms ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
K Nearest Neighbors ◽  
Cancer Prediction ◽  
Artificial Neural Network Ann

AbstractBreast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10-fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under receiver operating curve, sensitivity, and specificity of 0.95, 0.80, 0.91. Therefore, identification of breast cancer patients correctly would create care opportunities such as monitoring and adopting intervention plans may benefit the quality of care in long-term.

Machine Learning Predictive Models to Estimate the UCS and Tensile Strength of Rocks in Bakken Field

2021 ◽  
Author(s):  
Abderraouf Chemmakh
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Machine Learning Algorithms ◽  
Petroleum Engineering ◽  
Average Error ◽  
Support Vector ◽  
Short Period ◽  
Wide Range

Abstract Uniaxial Compressive Strength (UCS) and Tensile Strength (TS) are among the essential rock parameters required and determined for rock mechanical studies in Petroleum Engineering. However, the determination of such parameters requires some laboratory experiments, which may be time-consuming and costly at the same time. In order to estimate these parameters efficiently and in a short period, some mathematical tools have been used by different researchers. When regression tools proved to give good results only in the limited range of data used, machine learning methods proved to be very accurate in generating models that can cover a wide range of data. In this study, two machine learning models were used to predict the UCS and TS, Support Vector Regression optimized by Genetic Algorithm (GA-SVR) and Artificial Neural Networks (ANNs). The results were discussed for both uniaxial compressive strength and tensile strength in terms of coefficient of determination R2, root mean squared error (RMSE) and mean average error (MAE). First, for the case of UCS, values of 0.99 and 0.99, values of 3.41 and 2.9 and values of 2.43 and 1.9 were obtained for R2, RMSE and MAE for the ANN and GA-SVR, respectively. Second, for the TS, the same analogy was followed, a coefficient R2 of 0.99 and 0.99, RMSE values of 0.41 and 0.45 and MAE values of 0.30 and 0.39 were obtained for ANNs and GA-SVR, respectively. The next step was to assess these models on a different dataset consisting of data obtained from Bakken Field in Williston Basin, North Dakota, United States. The models showed excellent results comparing to the correlations they were compared with, outperforming them in terms of R2, RMSE and MAE, giving the following results for ANN and SVR respectively, R2 of 0.93, 0.92, RMSE of 9.54, 11.22 and MAE of 7.28, 9.24. The resultant conclusion of this work is that the use of machine learning algorithms can generate universal models which reduce the time and effort to estimate some complex parameters such as UCS and Tensile Strength.

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