Estimation of missing weather variables using different data mining techniques for avalanche forecasting

Kaur, Prabhjot; Joshi, Jagdish Chandra; Aggarwal, Preeti

doi:10.1007/s11069-024-06406-6

Cited by 3 publications

References 51 publications

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Assessment of Different Methods for Estimation of Missing Rainfall Data

Hırca,

Eryılmaz Türkkan

2024

Water Resour Manage

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Missing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.

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Assessment of Different Methods for Estimation of Missing Rainfall Data

Hırca,

Eryılmaz Türkkan

2024

Water Resour Manage

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An Innovative Approach to Filling Gaps in Wind Speed Data: A Systematic Literature Review

Hartika,

Permanasari,

Fauziati

2024

2024 2nd International Symposium on Information Technology and Digital Innovation (ISITDI)

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Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

Kashongwe,

Kabelitz,

Ammon

et al. 2024

AgriEngineering

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Missing data and class imbalance hinder the accurate prediction of rare events such as dairy mastitis. Resampling and imputation are employed to handle these problems. These methods are often used arbitrarily, despite their profound impact on prediction due to changes caused to the data structure. We hypothesize that their use affects the performance of ML models fitted to automated milking systems (AMSs) data for mastitis prediction. We compare three imputations—simple imputer (SI), multiple imputer (MICE) and linear interpolation (LI)—and three resampling techniques: Synthetic Minority Oversampling Technique (SMOTE), Support Vector Machine SMOTE (SVMSMOTE) and SMOTE with Edited Nearest Neighbors (SMOTEEN). The classifiers were logistic regression (LR), multilayer perceptron (MLP), decision tree (DT) and random forest (RF). We evaluated them with various metrics and compared models with the kappa score. A complete case analysis fitted the RF (0.78) better than other models, for which SI performed best. The DT, RF, and MLP performed better with SVMSMOTE. The RF, DT and MLP had the overall best performance, contributed by imputation or resampling (SMOTE and SVMSMOTE). We recommend carefully selecting resampling and imputation techniques and comparing them with complete cases before deciding on the preprocessing approach used to test AMS data with ML models.

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Estimation of missing weather variables using different data mining techniques for avalanche forecasting

Cited by 3 publications

References 51 publications

Assessment of Different Methods for Estimation of Missing Rainfall Data

Assessment of Different Methods for Estimation of Missing Rainfall Data

An Innovative Approach to Filling Gaps in Wind Speed Data: A Systematic Literature Review

Influence of Preprocessing Methods of Automated Milking Systems Data on Prediction of Mastitis with Machine Learning Models

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