scholarly journals Predicting Daily Pore Water Pressure in Embankment Dam Using Machine Learning Models and Hydrostatic Seasonal Time Approach

2021 ◽  
Author(s):  
Ali El Bilali ◽  
Mohammed Moukhliss ◽  
Abdeslam Taleb ◽  
Ayoub Nafii ◽  
Bahija Alabjah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Support Vector ◽  
Embankment Dam ◽  
Adaptive Boosting ◽  
Study Results ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive

Abstract Prediction-based approaches are valuable in assessing dam safeties, as they allow comparing the actual measurements with the projected values to detect anomalies early. For two decades, machine learning (ML) algorithms have been developed and improved to help in accurately predicting the dam behaviors. However, the generalization ability (GA) of these models is not analyzed enough in dam engineering. In this study, the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), Support Vector Regression (SVR), and Adaptive Boosting (AdaBoost) models with nonlinear autoregressive exogenous inputs (NARX) are evaluated and compared with the conventional Hydrostatic Seasonal Time (HST) model for predicting the daily pore water pressure in an embankment Dam. Moreover, we proposed a classification method of the model into four categories ‘’Perfect’’, ‘’Excellent’’, ‘’Good’’, and ‘’Poor’’ according to the GA. Results showed that, except for the AdaBoost, the other ML models outperformed the traditional statistical approach (HST) in terms of prediction accuracy as well as the GA. Overall; the study results provide new insights in enhancing the monitoring processes and dam safeties by detecting the anomalies early through the measurements and the selection of the best fitted-models.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

Prediction of pore-water pressure response to rainfall using support vector regression

2016 ◽  
Vol 24 (7) ◽  
pp. 1821-1833 ◽  
Author(s):  
Nuraddeen Muhammad Babangida ◽  
Muhammad Raza Ul Mustafa ◽  
Khamaruzaman Wan Yusuf ◽  
Mohamed Hasnain Isa
Keyword(s):  
Support Vector Regression ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Pressure Response ◽  
Support Vector

scholarly journals Evaluation of low degree polynomial kernel support vector machines for modelling Pore-water pressure responses

2016 ◽  
Vol 59 ◽  
pp. 04003
Author(s):  
Nuraddeen Muhammad Babangida ◽  
Muhammad Raza Ul Mustafa ◽  
Khamaruzaman Wan Yusuf ◽  
Mohamed Hasnain Isa ◽  
Imran Baig
Keyword(s):  
Support Vector Machines ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Degree Polynomial ◽  
Low Degree ◽  
Vector Machines ◽  
Kernel Support Vector Machines

scholarly journals Interpretasi Instrumentasi Piezometer Dalam Rangka Pemantauan Keamanan Bendungan Kedung Ombo

2021 ◽  
Vol 7 (2) ◽  
pp. 131-145
Author(s):  
Gerald Guntur Pandapotan Siregar ◽  
Fajar aldoko Kurniawan
Keyword(s):  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Water Level Fluctuations ◽  
Embankment Dam ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Embankment Dams ◽  
The World ◽  
Earthfill Dams

The embankment dam is the most widely built dam in the world, especially in Indonesia. However, embankment dams are also prone to collapse. Dam failures due to the piping process through the dam body account for 30.5% of the total dam collapses worldwide. Therefore, it is necessary to periodically monitor and evaluate the condition of pore water pressure and seepage in a dam which is usually carried out using installed instrumentation. Very little has been done on instrumentation interpretation of earthfill dams in Indonesia, which is a very worrying condition. It is possible that old or even new dams have shown behavior that leads to a decrease in safety. This condition can be monitored by instrumentation in the dam if interpreted properly. Kedung Ombo Dam as an old embankment dam but has a fairly complete instrumentation can be evaluated for safety related to pore water pressure and phreatic line (seepage line). Pore water pressure evaluation is carried out by collecting piezometer readings and reservoir water level fluctuations over a period of several years. The results of the research on the interpretation of piezometer readings indicate that the overall safety of the Kedung Ombo dam is still good in terms of pore water pressure conditions. However, there are some anomalous conditions that should be investigated further

scholarly journals Influence of pore water pressure to seepage and stability of embankment dam (case study of Sermo Dam Yogyakarta, Indonesia)

2017 ◽  
Vol 101 ◽  
pp. 05007
Author(s):  
Undayani Cita Sari ◽  
Sri Prabandiyani Retno Wardani ◽  
Suharyanto ◽  
Windu Partono
Keyword(s):  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Embankment Dam

scholarly journals ANALISIS STABILITAS TIMBUNAN (MAINDAM) BERDASARKAN DATA INSTRUMEN GEOTEKNIK PADA BENDUNGAN SINDANG HEULA SERANG BANTEN

2021 ◽  
Vol 3 (1) ◽  
pp. 48-58
Author(s):  
Nanang Sutisna ◽  
M. Ichwanul Yusup ◽  
Euis Amilia Euis Amilia
Keyword(s):  
Pore Water ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Field Studies ◽  
Internal Erosion ◽  
Embankment Dam ◽  
Support Tool ◽  
The Core ◽  
Conducting Research ◽  
The Stability

The development of science and technology has obtained supporting technology for monitoring the soil shear force and pore water pressure in the dam, the presence of shear forces against the landfill and pore water pressure through small cavities in the embankment soil in the dam body which can be detected by equipment such as inclinometer and piezometer that have been installed at predetermined points. The application of inclinometer and piezometer technology is used as a support tool for monitoring the movement of landfill and pore water pressure against dams. The embankment dam is the most complex of civilian structures and is very dangerous if damaged. When there is damage to a dam, it will cause a big disaster for the areas that are downstream of the dam. Damage or collapse of a dam can occur due to several things, including overtopping, sliding of the dam slopes (internal erosion or "piping"), and the occurrence of structural degradation of each zone. on the dam body. In the analysis of the stability of the embankment (maindam) which is based on geotechnical instrument data, it must be carried out as carefully and accurately as possible. The purpose of this analysis is to measure the early damage in the main dam (maindam). After conducting research and field studies at the Sindang Heula dam, there were several points of decline at the top of the core embankment (maindam). To find out the cause of the decline, data was taken from measuring geotechnical instruments.

scholarly journals Classification by a stacking model using CNN features for COVID-19 infection diagnosis

2021 ◽  
pp. 1-16
Author(s):  
Yavuz Selim Taspinar ◽  
Ilkay Cinar ◽  
Murat Koklu
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Support Vector ◽  
Clinical Environment ◽  
X Ray ◽  
Left Behind ◽  
Study Results ◽  
Chest X Ray ◽  
Ann Models ◽  
Artificial Neural Network Ann

Affecting millions of people all over the world, the COVID-19 pandemic has caused the death of hundreds of thousands of people since its beginning. Examinations also found that even if the COVID-19 patients initially survived the coronavirus, pneumonia left behind by the virus may still cause severe diseases resulting in organ failure and therefore death in the future. The aim of this study is to classify COVID-19, normal and viral pneumonia using the chest X-ray images with machine learning methods. A total of 3,486 chest X-ray images from three classes were first classified by three single machine learning models including the support vector machine (SVM), logistics regression (LR), artificial neural network (ANN) models, and then by a stacking model that was created by combining these 3 single models. Several performance evaluation indices including recall, precision, F-score, and accuracy were computed to evaluate and compare classification performance of 3 single four models and the final stacking model used in the study. As a result of the evaluations, the models namely, SVM, ANN, LR, and stacking, achieved 90.2%, 96.2%, 96.7%, and 96.9%classification accuracy, respectively. The study results indicate that the proposed stacking model is a fast and inexpensive method for assisting COVID-19 diagnosis, which can have potential to assist physicians and nurses to better and more efficiently diagnose COVID-19 infection cases in the busy clinical environment.

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