Identifying relevant features of termite signals applied in termite detection system

Nanda, Muhammad Achirul; Maddu, Akhiruddin; Nandika, Dodi

doi:10.1016/j.ecoinf.2021.101391

Cited by 7 publications

(2 citation statements)

References 50 publications

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“…Boruta works as a wrapper algorithm around Random Forest [33]. This method is a suitable candidate for reducing the dimensionality of the data [34]. The Boruta algorithm uses the Out-of-Bag (OOB) error to define the important score of the input features [33].…”

Section: Boruta Methodsmentioning

confidence: 99%

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Nguyen

Seidu

2022

Water

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Predicting the condition of sewer pipes plays a vital role in the formulation of predictive maintenance strategies to ensure the efficient renewal of sewer pipes. This study explores the potential application of ten machine learning (ML) algorithms to predict sewer pipe conditions in Ålesund, Norway. Ten physical factors (age, diameter, depth, slope, length, pipe type, material, network type, pipe form, and connection type) and ten environmental factors (rainfall, geology, landslide area, population, land use, building area, groundwater, traffic volume, distance to road, and soil type) were used to develop the ML models. The filter, wrapper, and embedded methods were used to assess the significance of the input factors. A dataset consisting of 1159 inspected sewer pipes was used to construct the sewer condition models, and 290 remaining inspections were used to verify the models. The results showed that sewer material and age are the most significant factors, otherwise the network type is the least contributor affecting the sewer conditions in the study area. Among the considered ML models, the Extra Trees Regression (R2 = 0.90, MAE = 11.37, and RMSE = 40.75) outperformed the other ML models and it is recommended for predicting sewer conditions for the study area. The results of this study can support utilities and relevant agencies in planning predictive maintenance strategies for their sewer networks.

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Section: Boruta Methodsmentioning

confidence: 99%

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Nguyen

Seidu

2022

Water

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“…These methods usually have higher performance than filter-based methods, these approaches however are more time-consuming [77]. Details on some wrapper methods can be found in [78].…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

2022

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Assessment of the structural condition of sewer pipes is one of the critical steps in asset management and support investment decisions; therefore, structural condition models with high accuracy are important that can help the utility managers and other authorities correctly assess the current condition of the sewage network and effectively initiate maintenance and rehabilitation strategies. The main objective of this research is to assess the potential application of advanced machine learning (ML) for predicting the structural condition of sewer pipes with a case study in Ålesund city, Norway. Nine physical factors (i.e., age, diameter, depth, slope, length, pipe type, material, pipe form, and connection type) and ten environmental factors (i.e., rainfall, geology, landslide area, building area, population, land cover, groundwater level, traffic volume, distance from the road, and soil type) were used to assess the sewer structural condition employing seventeen ML models. After processing the sewer inspections, 1,159 of 1,449 individual pipelines were used to train the structural condition model. The performance of ML models was validated using the 290 remaining inspected sewer pipes. The area under the Receiver Operating Characteristic (AUC-ROC) curve and accuracy (ACC) showed that the Random Forest (AUC-ROC = 77.6% and ACC = 78.3%) is a sensitive model for predicting the condition of sewer pipes in the study area. Based on the RF model, maps of predicted conditions of sewers were generated that may be useful for utilities and water managers to establish future sewer system maintenance strategies.

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Changes in arsenic accumulation and metabolic capacity after environmental management measures in mining area

Zhao

Yang

et al. 2023

Science of The Total Environment

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Identifying relevant features of termite signals applied in termite detection system

Cited by 7 publications

References 50 publications

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Application of Regression-Based Machine Learning Algorithms in Sewer Condition Assessment for Ålesund City, Norway

Comparison of Machine Learning Techniques for Condition Assessment of Sewer Network

Changes in arsenic accumulation and metabolic capacity after environmental management measures in mining area

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