Mass wasting susceptibility assessment of snow avalanches using machine learning models

Choubin, Bahram; Borji, Moslem; Hosseini, Farzaneh Sajedi; Mosavi, Amir; Dineva, Adrienn

doi:10.1038/s41598-020-75476-w

Cited by 51 publications

(18 citation statements)

References 102 publications

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“…Recently, an emerging class of machine learning (ML) models, such as artificial neural networks (ANNs), random forest (RF), adaptive neuro-fuzzy inference-based system (ANFIS), gene expression programming (GEP), group method of data handling (GMDH), support vector machine (SVM), and ensemble ML models were proposed and successfully applied in the literature for surface water and groundwater quality prediction [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. The ANNs are the computational network models based on the biological neural network that forms the structure of human brain.…”

Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

et al. 2022

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The prediction accuracies of machine learning (ML) models may not only be dependent on the input parameters and training dataset, but also on whether an ensemble or individual learning model is selected. The present study is based on the comparison of individual supervised ML models, such as gene expression programming (GEP) and artificial neural network (ANN), with that of an ensemble learning model, i.e., random forest (RF), for predicting river water salinity in terms of electrical conductivity (EC) and dissolved solids (TDS) in the Upper Indus River basin, Pakistan. The projected models were trained and tested by using a dataset of seven input parameters chosen on the basis of significant correlation. Optimization of the ensemble RF model was achieved by producing 20 sub-models in order to choose the accurate one. The goodness-of-fit of the models was assessed through well-known statistical indicators, such as the coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), and Nash–Sutcliffe efficiency (NSE). The results demonstrated a strong association between inputs and modeling outputs, where R2 value was found to be 0.96, 0.98, and 0.92 for the GEP, RF, and ANN models, respectively. The comparative performance of the proposed methods showed the relative superiority of the RF compared to GEP and ANN. Among the 20 RF sub-models, the most accurate model yielded the R2 equal to 0.941 and 0.938, with 70 and 160 numbers of corresponding estimators. The lowest RMSE values of 1.37 and 3.1 were yielded by the ensemble RF model on training and testing data, respectively. The results of the sensitivity analysis demonstrated that HCO3− is the most effective variable followed by Cl− and SO42− for both the EC and TDS. The assessment of the models on external criteria ensured the generalized results of all the aforementioned techniques. Conclusively, the outcome of the present research indicated that the RF model with selected key parameters could be prioritized for water quality assessment and management.

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Section: Introductionmentioning

confidence: 99%

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

et al. 2022

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“…In the recent past, machine learning (ML) algorithms have achieved notable successes at efficiently solving real-world problems in different sectors, including civil and environmental engineering [ 12 ], geotechnical engineering [ 13 , 14 , 15 , 16 , 17 , 18 ], and other fields of science [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ]. The artificial neural network (ANN) approach was found to be more efficient in predicting the shear strength of RFMs [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

et al. 2022

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The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R2) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R2 = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.

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“…Predictive Analysis aims to accurately diagnose diseases, improve patient care, optimize resources, and improve clinical outcomes [2]. Machine learning is one of the most important aspects of artificial intelligence because it allows computers to learn from past experiences without being programmed [4,5]. Machine learning's realistic implementations drive business outcomes that can significantly impact the bottom line of a corporation.…”

Section: Introductionmentioning

confidence: 99%

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

Hossain¹,

Alshehri²,

Almakdi³

et al. 2022

Computers, Materials &Amp; Continua

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Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes. The Doctor Consultation Module ensures free treatment related to diabetes. In this research, 90% accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor's algorithm (KNN) & LightGBM. To evaluate the model's performance, Receiver Operating Characteristics (ROC) Curve and Area under the ROC Curve (AUC) were applied with a value of 0.948 and 0.936, respectively. This manuscript presents some exploratory data analysis, including a correlation matrix and survey report. Moreover, the proposed solution can be adjustable in the daily activities of a diabetic patient.

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Mass wasting susceptibility assessment of snow avalanches using machine learning models

Cited by 51 publications

References 102 publications

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

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