Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall

Sattari, Mohammad Taghi; Falsafian, Kambiz; Irvem, Ahmet; Shahab, Sana; Qasem, Sultan Noman

doi:10.1080/19942060.2020.1803971

Cited by 22 publications

(10 citation statements)

References 31 publications

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“…A further challenge with bias correction is the availability of daily streamflow data for the given timeline. Fortunately, this problem is not encountered in this study, although previous research has demonstrated the validity of applying a machine learning technique to interpolate missing data [70]. Additionally, bias correction was performed for river discharge strictly based on its historical datasets, which invites future studies to incorporate other hydrological parameters.…”

Section: Uncertainities and Limitationsmentioning

confidence: 99%

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

2022

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Climate change has caused uncertainty in the hydrological pattern including weather change, precipitation fluctuations, and extreme temperature, thus triggering unforeseen natural tragedies such as hurricanes, flash flooding, heatwave and more. Because of these unanticipated events occurring all around the globe, the study of the influence of climate change on the alteration of flooding patterns has gained a lot of attention. This research study intends to provide an insight into how the future projected streamflow will affect the flooding-inundation extent by comparing the change in floodplain using both historical and future simulated scenarios. For the future projected data, the climate model Atmosphere/Ocean General Circulation Model (AOGCM) developed by Coupled Model Intercomparison Project Phase 6 (CMIP6) is used, which illustrates that the flood is increasing in considering climate models. Furthermore, a comparison of the existing flood inundation map by the Federal Emergency Management Agency (FEMA) study with the map generated by future projected streamflow data presents the entire inundation area in flood maps, implying the expansion area compared to FEMA needs to be considered in making emergency response plans. The effect of flooding in the inundation area from historical to future flow values, presented mathematically by a calculation of inundation extent percentage, infers that the considered watershed of Rock River is a flood-prone area. The goal is to provide insights on the importance of using the forecasted data for flood analysis and to offer the necessary background needed to strategize an emergency response plan for flood management.

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Section: Uncertainities and Limitationsmentioning

confidence: 99%

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

2022

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Section: Modeling Of Wedm Processmentioning

confidence: 99%

“…SVM and the SVR model are the two primary categories of SVM models. A SVM model is used to classify data into multiple classes, while an SVR model is employed for prediction purposes (Sattari et al, 2020). Regression is used to obtain an appropriate hyperplane for the utilized data (Smola and Sch, 2004).…”

Section: Support Vector Regressionmentioning

confidence: 99%

Surface roughness modeling using machine learning approaches for wire electro-spark machining of titanium alloy

Sharma

Misra

Singhal

2022

IJSI

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PurposeIn the present study, wire electro-spark machining of Titanium alloy is performed with the machining parameter such as spark-on time, spark-off time, current and servo voltage. The purpose of this study is to model surface roughness using machine learning approach for input/controllable variable. Machined surface examined using scanning electron microscope (SEM) and XRD methods.Design/methodology/approachFull factorial approach has been used to design the experiments with varying machining parameters into three-level four factors. Obtained surface roughness was modeled using machine learning methods namely Gaussian process regression (GPR) and support vector machine (SVM) methods. These methods were compared for both training and testing data with a coefficient of correlation and root mean square error basis. Machined surface examined using scanned electron microscopy and XRD for surface quality produced and check migration of tool material to workpiece material.FindingsMachine learning algorithms has excellent scope for prediction quality response for the wire electric discharge machining (WEDM) process, resulting in saving of time and cost as it is difficult to find each time experimentally. It has been found that the proposed model with minimum computational time, provides better solution and avoids priority weightage calculation by decision-makers.Originality/valueThe proposed modeling provides better predication about surface produced while machining of Ti6Al7Nb using zinc-coated brass wire electrode during WEDM operation.

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“…The approaches considered were the sparse model for linear regression estimation (Predictor 1) and Random Forest Decision Tree (Predictor 2) due to their various benefits mentioned in numerous researches. [15][16][17] Predictor 1-Linear regression is a statistical approach where a target function is linear and depends on features as the input. All input attributes as real numbers and represented as:…”

Section: Mathematical Modeling Of the Machine Learning Algorithmmentioning

confidence: 99%

Machine learning predictor for micro gas turbine performance evaluation

Jayachandran¹,

Omar²,

Tkachenko³

et al. 2020

AAOAJ

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Due to the increased trends and innovation with reliable IoT sensors, high-speed networks, and supercomputers the amount of data solving has improved in predicting performance with greater accuracy. Machine learning-based diagnostics and health monitoring for large gas turbines are available but lack application in Micro gas turbine component design and performance prediction. In this paper to predict the optimized net efficiency using a machine learning-based model was developed. Micro gas turbine power plant mathematical model (MGTPPMM) was developed that could generate an electric power of 6kW which was validated using the operational conditions of Jet Cat PHT3. The resulting data was used to optimize and predict the net efficiency of gas turbines and also its compressor and turbine characteristics using a Machine learning algorithm model (MLAM). The MGTPPMM has an accuracy of 92% in simulating various operating conditions while two MLAM was developed Predictor 1 and Predictor 2. Predictor 1 has an accuracy of 76% and Predictor 2 has an accuracy of just less than1 % error at sea level operating conditions. However at higher altitudes greater than 17 Km operating condition the accuracy of the MGTPPMM is 88% while that of the MLAM -Predictor 2 has an error of less than 6% this is due to the lack of sufficient validated results data required for training sets. The proposed models may help to design more efficient micro gas turbines at the component level and also could predict performance characteristics to monitor the health of the system virtually by implementing a degradation monitor system.

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Potential of kernel and tree-based machine-learning models for estimating missing data of rainfall

Cited by 22 publications

References 31 publications

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling

Surface roughness modeling using machine learning approaches for wire electro-spark machining of titanium alloy

Machine learning predictor for micro gas turbine performance evaluation

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