Work

Imputation of Multi-Dimensional High-Frequency Climate Data to Predict Air and Surface Temperatures in Kuwait

Year: 2026

Type: article

Abstract: Missing values may arise in climate data collection due to sensor malfunction, transmission errors, device calibration and operational issues. This problem can be more catastrophic in the case of multi-dimensional and high-frequency climate data sets, where some or all climate readings could be missing at multiple timestamps. These missing data in high-frequency climate modeling could lead to inaccurate prediction models, which in turn affect overall assessments, planning, and climate-related measures and policy. In this paper, we evaluate the performance of three imputation techniques based on the mean, k-nearest neighbor, time-based interpolation and a new temporal cross-year climate imputation approach using a random forest, long short-term memory (LSTM) model and contextual embedding-based Transformer regression methods. We discussed our findings on four years of multi-output, high-frequency and multi-dimensional climate data collected in Kuwait. Using a leave-one-year-out cross-validation approach, our results show that all imputation methods perform better than no imputation, with LSTM and time-based interpolation emerging as the best combination. Imputing climate data based on previous years’ timestamps did not yield good results, highlighting the variability of climate data across years.

Source: Information

DOI: 10.3390/info17030221

Authors: Shehroz S. Khan, Rami Al-Hajj

Institution: American University of the Middle East

Language: English

Topic: Environmental Monitoring and Data Management

Subfield: Geology

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $1,930