When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Mordensky, Stanley; Lipor, John J.; DeAngelo, Jacob; Burns, Erick R.; Lindsey, Cary R.

doi:10.1016/j.geothermics.2023.102662

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Mordensky et al. (2023) applied several different ML methods to evaluate the geothermal energy assessments, which also found that complex ML methods (e.g., artificial neural networks) may not always outperform simple approaches (e.g., simple nonlinear algorithm).…”

Section: Discussionmentioning

confidence: 99%

“…Clearly, we should be cautious to not apply unnecessarily complex analysis methods when simple tools can achieve good results. Mordensky et al (2023) applied several different ML methods to the geothermal energy assessments, which also found that complex ML methods (e.g., artificial neural networks) may not always outperform simple approaches (e.g., simple nonlinear algorithm).…”

Section: Limitations and Future Directions For Data Driven Modelingmentioning

confidence: 99%

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Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Dempsey

Rinaldi³

et al. 2023

JGR Solid Earth

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Understanding injection‐induced microseismicity in geothermal systems can provide insight into reservoir connectedness. However, fault and reservoir complexity are difficult to represent in simple analytical models, which makes it difficult to discern clear relationships from incidental associations. Here, we have used data‐driven models to study how fluid injection and microseismicity are related in the Rotokawa (New Zealand) and Hellisheiði (Iceland) geothermal fields. We tested two classes of model: (a) lagged linear regression of seismicity rate as a function of well injection rates; and (b) systematic extraction of injection time series features that are then evaluated for associations with the seismicity. These models allowed us to determine which wells had the greatest correlation with microseismicity and to explain this association in a reservoir context. Finally, exploring different data types and transformations, we were unable to establish a link between rapid changes in injection rate and seismicity spikes, as suggested by some theoretical models.

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Section: Discussionmentioning

confidence: 99%

Section: Limitations and Future Directions For Data Driven Modelingmentioning

confidence: 99%

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Dempsey

Rinaldi³

et al. 2023

JGR Solid Earth

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“…This study only addressed the one ML algorithm which is LR by analyse in each single complex feature. This is due to the limitation of LR does not perform well when the relationship between the input features and the target feature is highly non-linear, or when there are complex interactions between the features (Mordensky et al, 2023). As a part of future work, the study will consider dealing the combination of complexity features by implementing more analysis on ML such as Support Vector Machines (Mayes et al, 2023) and Naïve Bayes (Sawhney et al, 2023), k-Nearest Neighbor (Singh et al, 2022) and Random Forest (Zafeiropoulos et al, 2023) in solving the problem of e-ticketing system.…”

Section: Discussionmentioning

confidence: 99%

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Mohd Jamaludin,

Majahar Ali,

Vun

et al. 2023

Mal. J. Fund. Appl. Sci.

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e-ticketing is one of the common applications used in technical support in Information Technology (IT) and has been used worldwide in any field of company. The benefits of e-ticketing can reduce the human efforts, increase the sufficiency of system and provides the benefits and efficiency to the customers. Also, e-ticketing ticketing system can enhance worker safety, improving productivity, increasing project efficiency and cause the good impact on the performance of the business in terms of profitability. The main objective of this study is defining the model performance in each important feature by analysing the complex dataset by using logistic regression as a Machine Learning (ML) algorithm. In evaluation the performance of classifier, the dataset is injected to Python programming and split into 90% as training set and 10% for the testing set. From the analysis, the study found that only 3 out of 11 independent features in dataset that are relevant chosen to proceed the ML analysis. From the result, the accuracy for sct_short_description, sct_cmdb_ci, and sct_assignment_group is 41.65%, 48.77% and 96.49%, respectively. It showed that the accuracy’s result for the sct_assignment_group resulted that the model is very good accuracy and indicate that the model is well performing. Meanwhile, the value of F1-score is 96.11% in each feature. This result indicates that the model has a good balance of precision and recall in its binary classification predictions. Hence, the study considers the sct_assignment_group as a best features to proceed the analysis. The future study will consider dealing the combination of complexity features by implementing more analysis on ML such as Support Vector Machines and Naïve Bayes.

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“…As a supervised learning algorithm, Neural Networks (NN) implement distributed parallel information processing by imitating the behavior of animal neural networks, including the input layer, hidden layer, and output layer (Stanley et al., 2023). NN can automatically learn from data without manual encoding or prior information.…”

Section: Deep Neural Network Methodsmentioning

confidence: 99%

Prediction of Terrestrial Heat Flow in Songliao Basin Based on Deep Neural Network

Bai,

Li,

Zeng

et al. 2023

Earth and Space Science

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Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high‐temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance‐based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low‐velocity sedimentary layer. Some high‐temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional‐scale heat flow, providing reliable heat source information for evaluating geothermal resources.

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When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Cited by 9 publications

References 14 publications

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Association Between Injection and Microseismicity in Geothermal Fields With Multiple Wells: Data‐Driven Modeling of Rotokawa, New Zealand, and Húsmúli, Iceland

Solving Complexity Dataset in e-Ticketing using Machine Learning to Determine Optimum Feature

Prediction of Terrestrial Heat Flow in Songliao Basin Based on Deep Neural Network

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