DOI: 10.7763/IJCTE.2026.V18.1394
Interpretable Machine Learning for Ambient Temperature Prediction: Insights from SHAP, ICE, PDP, and ALE
2. Water Resources Management and Soft Computing Research Laboratory, Millennium City, Athurugiriya, 10150, Sri Lanka
3. Department of Chemistry, Faculty of Science, University of Kelaniya, 11600, Sri Lanka
4. Department of Civil Engineering, Faculty of Engineering, University of Peradeniya, Peradeniya, Sri Lanka
5. Advanced Institute for Marine Ecosystem Change (WPI-AIMEC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama 236-0001, Japan
6. United Nations-SPIDER-UK Regional Support Office, Preston PR1 2HE, United Kingdom
7. School of Engineering and Computing, University of Lancashire, Preston PR1 2HE, United Kingdom
Email: hirushansajindra96@gmail.com (H.S.); thilinaabekoon@gmail.com (T.A.); skbuthpitiya@gmail.com (S.B.); yasithaalahakoon98@gmail.com (Y.A.); namalr@jamstec.go.jp (N.R.); KKantamaneni@uclan.ac.uk (K.K.); upaka.rathnayake@atu.ie (U.R.)
*Corresponding author
Manuscript received August 29, 2025; revised December 15, 2025; accepted March 19, 2026; published May 16, 2026
Abstract—Accurate ambient temperature prediction is essential for climate monitoring, urban planning, and environmental management, particularly in regions experiencing rapid climatic variability such as Sri Lanka. This study investigates the application of explainable machine learning models for short-term ambient temperature prediction in Battaramulla, Sri Lanka. Five regression algorithms-K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Regression (SVR), and Histogram-based Gradient Boosting Regressor (HGBR) were evaluated using 14 meteorological and environmental predictors, including temporal variables, relative humidity, solar radiation, rainfall, wind speed, and air pollutant concentrations (CO2, NOₓ, CH4, O3, CO, PM2.5, and PM10). Among the models tested, HGBR demonstrated superior predictive performance, achieving R² values of 1.00 (training) and 0.96 (testing), with corresponding mean squared error values of 0.01 and 0.11. Model interpretability was examined using SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) analyses, and Accumulated Local Effects (ALE), which identified several features as the most influential predictors. Model validation using 192 real-time observations showed close agreement between predicted and measured temperatures, although the evaluation was limited to a single location and time period. A web-based application, ‘Therma’, was developed to facilitate practical deployment of the model for localized temperature estimation. Overall, this study demonstrates the utility of explainable machine learning for localized climate prediction while highlighting the need for broader spatiotemporal validation in future work.
Keywords—ambient temperature, explainable artificial intelligence, Histogram-Based Gradient Boosting Regression (HGBR), prediction
Cite: Hirushan Sajindra, Thilina Abekoon, Salani Buthpitiya, Yasitha Alahakoon, Namal Rathnayake, Komali Kantamaneni, and Upaka Rathnayake, "Interpretable Machine Learning for Ambient Temperature Prediction: Insights from SHAP, ICE, PDP, and ALE," International Journal of Computer Theory and Engineering, vol. 18, no. 2, pp. 118-132, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).