Predicting the Effects of Climate Change on Water Temperatures of Roode Elsberg Dam Using Nonparametric Machine Learning Models

A nonparametric machine learning model was used to study the behaviour of the variables of a concrete arch dam: Roode Elsberg dam. The variables used were ambient temperature, water temperatures, and water level. Water temperature was measured using twelve thermometers; six thermometers were on each flank of the dam. The thermometers were placed in pairs on different levels: avg6 (avg6-R and avg6-L) and avg5 (avg5-R and avg5-L) were on level 47.43 m, avg4 (avg4-R and avg4-L) and avg3 (avg3-R and avg3-L) were on level 43.62 m, and avg2 (avg2-R and avg2-L) and avg1 (avg1-R and avg1-L) were on level 26.23 m. Four neural networks and four random forests were cross-validated to determine their best-performing hyperparameters with the water temperature data. Quantile random forest was the best performer at mtry 7 (Number of variables randomly sampled as candidates at each split) and RMSE (Root mean square error) of 0.0015, therefore it was used for making predictions. The predictions were made using two cases of water level: recorded water level and full dam steady-state at Representative Concentration Pathway (RCP) 4.5 (hot and cold model) and RCP 8.5 (hot and cold model). Ambient temperature increased on average by 1.6 °C for the period 2012–2053 when using recorded water level; this led to increases in water temperature of 0.9 °C, 0.8 °C, and 0.4 °C for avg6-R, avg3-R, and avg1-R, respectively, for the period 2012–2053. The same average temperature increase led to average increases of 0.7 °C for avg6-R, 0.6 °C for avg3-R, and 0.3 °C for avg1-R for a full dam steady-state for the period 2012–2053.

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Machine learning-based digital twins reduce seasonal remapping in aeroderivative gas turbines

Journal of Energy Resources Technology ◽

10.1115/1.4052994 ◽

2021 ◽

pp. 1-7

Author(s):

Nick Petro ◽

Felipe Lopez

Keyword(s):

Machine Learning ◽

Ambient Temperature ◽

Gas Turbines ◽

Operating Conditions ◽

Pilot Site ◽

Digital Twin ◽

Digital Twins ◽

Machine Learning Model ◽

Unacceptable Behavior ◽

External Conditions

Abstract Aeroderivative gas turbines have their combustion set points adjusted periodically in a process known as remapping. Even turbines that perform well after remapping may produce unacceptable behavior when external conditions change. This article introduces a digital twin that uses real-time measurements of combustor acoustics and emissions in a machine learning model that tracks recent operating conditions. The digital twin is leveraged by an optimizer that select adjustments that allow the unit to maintain combustor dynamics and emissions in compliance without seasonal remapping. Results from a pilot site demonstrate that the proposed approach can allow a GE LM6000PD unit to operate for ten months without seasonal remapping while adjusting to changes in ambient temperature (4 - 38 °C) and to different fuel compositions.

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Application of Machine Learning to Interpret Steady State Drainage Relative Permeability Experiments

10.2118/207877-ms ◽

2021 ◽

Author(s):

Eric Sonny Mathew ◽

Moussa Tembely ◽

Waleed AlAmeri ◽

Emad W. Al-Shalabi ◽

Abdul Ravoof Shaik

Keyword(s):

Neural Network ◽

Machine Learning ◽

Experimental Data ◽

Steady State ◽

Relative Permeability ◽

Learning Model ◽

Gradient Boosting ◽

Data Set ◽

Machine Learning Model ◽

Extreme Gradient Boosting

Abstract A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, three different machine learning models was developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. The three different models that were tested and compared were extreme gradient boosting (XGB), deep neural network (DNN) and recurrent neural network (RNN) algorithms. Based on existing mathematical models, a leading edge framework was developed where a large database of Kr and Pc curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were utilized to estimate Kr curves based on Darcy's law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the machine learning model. The entire data set was split into 80% for training and 20% for testing. K-fold cross validation technique was applied to increase the model accuracy by splitting the 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, 9 folds were used for training and the remaining one was used for model validation. Once the model is trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The machine learning model learns to capture fluid flow behavior inside the core from the training dataset. The trained/tested model was thereby employed to estimate Kr curves based on available experimental results. The performance of the developed model was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective cross plots along with comparisons of ground-truth versus AI predicted curves indicate that the model is capable of making accurate predictions with error percentage between 0.2 and 0.6% on history matching experimental data for all the three tested ML techniques (XGB, DNN, and RNN). This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The results also include a comparison between classical machine learning approaches, shallow and deep neural networks in terms of accuracy in predicting the final Kr curves. The various models discussed in this research work currently focusses on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

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Forecasting water level of Glacial fed perennial river using a genetically optimized hybrid Machine learning model

Materials Today Proceedings ◽

10.1016/j.matpr.2021.02.256 ◽

2021 ◽

Author(s):

Mirza Imran ◽

P. Sheikh Abdul Khader ◽

Mohammd Rafiq ◽

Kishan Singh Rawat

Keyword(s):

Machine Learning ◽

Water Level ◽

Learning Model ◽

Machine Learning Model ◽

Hybrid Machine

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Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta

10.5194/npg-2021-36 ◽

2022 ◽

Author(s):

Joko Sampurno ◽

Valentin Vallaeys ◽

Randy Ardianto ◽

Emmanuel Hanert

Keyword(s):

Machine Learning ◽

Random Forest ◽

Water Level ◽

Integrated Approach ◽

Learning Model ◽

Machine Learning Algorithms ◽

Support Vector ◽

Flood Hazards ◽

Area Of Interest ◽

Machine Learning Model

Abstract. Flood forecasting based on water level modeling is an essential non-structural measure against compound flooding over the globe. With its vulnerability increased under climate change, every coastal area became urgently needs a water level model for better flood risk management. Unfortunately, for local water management agencies in developing countries building such a model is challenging due to the limited computational resources and the scarcity of observational data. Here, we attempt to solve the issue by proposing an integrated hydrodynamic and machine learning approach to predict compound flooding in those areas. As a case study, this integrated approach is implemented in Pontianak, the densest coastal urban area over the Kapuas River delta, Indonesia. Firstly, we built a hydrodynamic model to simulate several compound flooding scenarios, and the outputs are then used to train the machine learning model. To obtain a robust machine learning model, we consider three machine learning algorithms, i.e., Random Forest, Multi Linear Regression, and Support Vector Machine. The results show that this integrated scheme is successfully working. The Random Forest performs as the most accurate algorithm to predict flooding hazards in the study area, with RMSE = 0.11 m compared to SVM (RMSE = 0.18 m) and MLR (RMSE = 0.19 m). The machine-learning model with the RF algorithm can predict ten out of seventeen compound flooding events during the testing phase. Therefore, the random forest is proposed as the most appropriate algorithm to build a reliable ML model capable of assessing the compound flood hazards in the area of interest.

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A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning Model

Water Resources Management ◽

10.1007/s11269-019-2193-8 ◽

2019 ◽

Vol 33 (4) ◽

pp. 1609-1628 ◽

Cited By ~ 8

Author(s):

Maryam Malekzadeh ◽

Saeid Kardar ◽

Keivan Saeb ◽

Saeid Shabanlou ◽

Lobat Taghavi

Keyword(s):

Machine Learning ◽

Ground Water ◽

Water Level ◽

Learning Model ◽

Ground Water Level ◽

Novel Approach ◽

Machine Learning Model ◽

Self Adaptive

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Water Level Prediction of Rainwater Pipe Network Using an SVM-Based Machine Learning Method

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420510027 ◽

2019 ◽

Vol 34 (02) ◽

pp. 2051002 ◽

Cited By ~ 2

Author(s):

Hao Wang ◽

Lixiang Song

Keyword(s):

Machine Learning ◽

Water Level ◽

Water Levels ◽

Machine Learning Method ◽

Learning Method ◽

Pipe Network ◽

Hydrodynamic Models ◽

Time Step ◽

Running Speed ◽

Machine Learning Model

Model accuracy and running speed are the two key issues for flood warning in urban areas. Traditional hydrodynamic models, which have a rigorous physical mechanism for flood routine, have been widely adopted for water level prediction of rainwater pipe network. However, with the amount of pipes increasing, both the running speed and data availability of hydrodynamic models would be decreased rapidly. To achieve a real-time prediction for the water level of the rainwater pipe network, a new framework based on a machine learning method was proposed in this paper. The spatial and temporal autocorrelation of water levels for adjacent manholes was revealed through theoretical analysis, and then a support vector machine (SVM)-based machine learning model was developed, in which the water levels of adjacent manholes and rivers-near-by-outlets at the last time step were chosen as the independent variables, and then the water levels at the current time step can be computed by the proposed machine learning model with calibrated parameters. The proposed framework was applied in Fuzhou city, China. It turns out that the proposed machine learning method can forecast the water level of the rainwater pipe network with good accuracy and running speed.

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Machine Learning Prediction of Journal Bearing Pressure Distributions, Considering Elastic Deformation and Cavitation

10.1115/fpmc2021-68483 ◽

2021 ◽

Author(s):

Nathan Hess ◽

Lizhi Shang

Keyword(s):

Neural Network ◽

Machine Learning ◽

Steady State ◽

Elastic Deformation ◽

Reynolds Equation ◽

Journal Bearing ◽

Fluid Power ◽

Pressure Distributions ◽

The Neural Network ◽

Machine Learning Model

Abstract This paper presents a machine learning neural network capable of approximating pressure as the distributive result of elastohydrodynamic effects and discusses results for a journal bearing at steady state. Design of efficient, reliable fluid power pumps and motors requires accurate models of lubricating interfaces; however, most existing simulation models are structured around numerical solutions to the Reynolds equation which involve nested iterative loops, leading to long simulation durations and limiting the ability to use such models in optimization studies. This study presents the development of a machine learning model capable of approximating the pressure solution of the Reynolds equation for given distributive geometric boundary conditions and considering cavitation and elastic deformation at steady-state operating conditions. The architecture selected for this study was an 8-layer U-Net convolutional neural network. A case study of a journal bearing was considered, and a 438-sample training set was generated using an in-house multiphysics simulator. After training, the neural network predicted pressure distributions for test samples with great accuracy, and accurately estimated resultant loads on the journal bearing shaft. Additionally, the neural network showed promise in analyzing geometric inputs outside the space of the training data, approximating the pressure in a grooved journal bearing with reasonable accuracy. These results demonstrate the potential to integrate a machine learning model into fluid power pump and motor simulations for faster performance during evaluation and optimization.

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