Advanced Data Science and Computational Intelligence

(Annual (One issue per year))

Dr Chalachew Muluken Liyew is a researcher and academic specializing in modelling, data science, machine learning, artificial intelligence, and climate data analysis. He earned his Ph.D. in Modelling and Data Science from the University of Turin (UNITO), Italy, where his research focused on statistical modelling and machine learning approaches for analysing climate and environmental data. He also holds an MSc in Information Technology from the University of Gondar and BSc degrees in Computer Science and Physics from Bahir Dar University, Ethiopia. He currently works at Bahir Dar University Institute of Technology as a researcher and lecturer, contributing to teaching, research, supervision, and academic activities. His research interests include statistical modelling, machine learning, deep learning, explainable artificial intelligence (XAI), data analytics, time-series forecasting, climate variability analysis, environmental modelling, hydrology, water resource management, and agricultural systems. His current research focuses on developing advanced, data-driven and explainable AI solutions to address complex challenges in climate change, environmental sustainability, and related interdisciplinary fields, supporting evidence-based decision-making and sustainable development.

• Statistical Modeling
• Feature Engineering
• Data visualization
• Deep learning
• Data Analytics
• Machine Learning

• Research in statistical modelling and machine learning for climate and environmental data analysis.
• Development of machine learning models for rainfall prediction and actual evapotranspiration forecasting.
• Climate trend and variability analysis using multivariate and time-series data.
• Research on air-temperature trends and patterns across highland and lowland regions of Italy and the UK.
• Research on feature selection and hyperparameter optimization for machine learning models.
• Application of data science and AI techniques to environmental and climate-related challenges.
• Interdisciplinary research in climate change, environmental sustainability, hydrology, water resource management, and agricultural systems.
• Publication of research findings in international peer-reviewed journals and conference proceedings.

1. Machine learning techniques to predict daily rainfall amount — 2021
2. Multivariate Time Series Evapotranspiration Forecasting Using Machine Learning Techniques — 2023
3. Analysis of Diurnal Air Temperature Trends and Pattern Similarities in Highland and Lowland Stations of Italy and UK — 2024
4. Identifying Time Patterns of Highland and Lowland Air Temperature Trends in Italy and UK across monthly and annual scales — 2024
5. The Impact of Technology Platform Change on the Information-Seeking Behavior of Academicians in Amhara — 2019
6. Hyperparameter optimization of machine learning models for predicting actual evapotranspiration — 2025
7. A review of feature selection methods for actual evapotranspiration prediction — 2025