Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods
Slope displacement monitoring is essential for assessing slope stability and preventing catastrophic failures, particularly in geotechnically sensitive areas. However, continuous data collection is often disrupted by environmental factors, sensor malfunctions, and communication issues, leading to mi...
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MDPI AG
2025-01-01
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| author | Seungjoo Lee Yongjin Kim Bongjun Ji Yongseong Kim |
| author_facet | Seungjoo Lee Yongjin Kim Bongjun Ji Yongseong Kim |
| author_sort | Seungjoo Lee |
| collection | DOAJ |
| description | Slope displacement monitoring is essential for assessing slope stability and preventing catastrophic failures, particularly in geotechnically sensitive areas. However, continuous data collection is often disrupted by environmental factors, sensor malfunctions, and communication issues, leading to missing data that can compromise analysis accuracy and reliability. This study addresses these challenges by evaluating advanced machine learning models—SAITS, ImputeFormer, and BRITS (Bidirectional Recurrent Imputation for Time Series)—for missing data imputation in slope displacement datasets. Sensors installed at two field locations, Yangyang and Omi, provided high-resolution displacement data, with approximately 34,000 data points per sensor. We simulated missing data scenarios at rates of 1%, 3%, 5%, and 10%, reflecting both random and block missing patterns to mimic realistic conditions. The imputation performance of each model was evaluated using Mean Absolute Error, Mean Squared Error, and Root Mean Square Error to assess accuracy and robustness across varying levels of data loss. Results demonstrate that each model has distinct advantages under specific missingness patterns, with the ImputeFormer model showing strong performance in capturing long-term dependencies. These findings underscore the potential of machine learning-based imputation methods to maintain data integrity in slope displacement monitoring, supporting reliable slope stability assessments even in the presence of significant data gaps. This research offers insights into the optimal selection and application of imputation models for enhancing the quality and continuity of geotechnical monitoring data. |
| format | Article |
| id | doaj-art-32e19563518942adb2de976768d654e6 |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-32e19563518942adb2de976768d654e62025-01-24T13:26:17ZengMDPI AGBuildings2075-53092025-01-0115223610.3390/buildings15020236Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation MethodsSeungjoo Lee0Yongjin Kim1Bongjun Ji2Yongseong Kim3Korean Peninsula Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of KoreaSmart E&C, Chuncheon 24341, Republic of KoreaGraduate School of Data Science, Pusan National University, Busan 46241, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon 24341, Republic of KoreaSlope displacement monitoring is essential for assessing slope stability and preventing catastrophic failures, particularly in geotechnically sensitive areas. However, continuous data collection is often disrupted by environmental factors, sensor malfunctions, and communication issues, leading to missing data that can compromise analysis accuracy and reliability. This study addresses these challenges by evaluating advanced machine learning models—SAITS, ImputeFormer, and BRITS (Bidirectional Recurrent Imputation for Time Series)—for missing data imputation in slope displacement datasets. Sensors installed at two field locations, Yangyang and Omi, provided high-resolution displacement data, with approximately 34,000 data points per sensor. We simulated missing data scenarios at rates of 1%, 3%, 5%, and 10%, reflecting both random and block missing patterns to mimic realistic conditions. The imputation performance of each model was evaluated using Mean Absolute Error, Mean Squared Error, and Root Mean Square Error to assess accuracy and robustness across varying levels of data loss. Results demonstrate that each model has distinct advantages under specific missingness patterns, with the ImputeFormer model showing strong performance in capturing long-term dependencies. These findings underscore the potential of machine learning-based imputation methods to maintain data integrity in slope displacement monitoring, supporting reliable slope stability assessments even in the presence of significant data gaps. This research offers insights into the optimal selection and application of imputation models for enhancing the quality and continuity of geotechnical monitoring data.https://www.mdpi.com/2075-5309/15/2/236data imputationmachine learninglandslidedisasterdata qualityprediction |
| spellingShingle | Seungjoo Lee Yongjin Kim Bongjun Ji Yongseong Kim Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods Buildings data imputation machine learning landslide disaster data quality prediction |
| title | Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods |
| title_full | Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods |
| title_fullStr | Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods |
| title_full_unstemmed | Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods |
| title_short | Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods |
| title_sort | addressing missing data in slope displacement monitoring comparative analysis of advanced imputation methods |
| topic | data imputation machine learning landslide disaster data quality prediction |
| url | https://www.mdpi.com/2075-5309/15/2/236 |
| work_keys_str_mv | AT seungjoolee addressingmissingdatainslopedisplacementmonitoringcomparativeanalysisofadvancedimputationmethods AT yongjinkim addressingmissingdatainslopedisplacementmonitoringcomparativeanalysisofadvancedimputationmethods AT bongjunji addressingmissingdatainslopedisplacementmonitoringcomparativeanalysisofadvancedimputationmethods AT yongseongkim addressingmissingdatainslopedisplacementmonitoringcomparativeanalysisofadvancedimputationmethods |