CLIMATE INFORMATICS RESEARCH PROJECTS

Faculty of Information Science and Technology &

Institute of Climate Change Universiti Kebangsaan Malaysia

Project Aim

Our research focuses on employing Data Mining and Optimisation algorithms to search for important patterns in climate change data for prediction. The issues of big climate data are encountered towards producing climate predictive model. Severe weather impacts the lives of millions of people each year. The protection, planning, and response to these challenges are central to Malaysia mitigation, and recovery which is often atop public perception and occupies many of nation resources. Tools to aid in this mission are important for many reasons. Better preparedness and improved recovery can help save lives, reduce costs, and provide comfort.

The main features of rainfall or weather data are the time series or temporal characteristic. In another word, the data are collected on time basis and it can generate the streams of data. The data streams include large amount of data that are collected in minutes or hourly. Therefore, the most important issue in discovering patterns in this kind of data is the data representation, pattern detection and predictive modeling Many machine learning and optimisation approaches have potential to be employed due to their efficiency. Furthermore they have proven to have superior performance in addressing optimisation problems.

Examples of optimisation algorithms that have been successful employed on related problem domains are modified bat algorithm for rainfall prediction problems, harmony search algorithm for symbolic time series prediction, k-nearest neighbour approach for time series data, and multi-objective particle swarm optimisation for numerical association rules in data mining. The successes of optimisation approaches offer rooms of opportunity to further explore their capability in various combinatorial optimisation problems particulaly on the climate change prediction problem. The developmentsof state of the art algorithms and techniques have dramatically improved severe weather detection.

Prof. Dato Dr. Sharifah Mastura Syed Ahmad Prof. Dr. Abdul Razak Hamdan Prof. Dr. Azuraliza Abu Bakar Prof. Dr. Salwani Abdullah Prof. Madya Dr. Zulaiha Ali Ohtman Prof. Madya Dr. Zalinda Othman Prof. Madya Dr. Ir. Othman Jaafar

Postgraduate Students Najmeh Sadat Jaddi

Nabilah Filzah Mohd. Radzuan

Almahdi Mohammed Ahmed

Siti Nur Kamaliah Kamarudin

Ghassan Saleh Al-Dharhani

Yahia Mohammed Benyahmed

Shakirah Mohd Taib

Maryam Mousavi

Our Researchers

Funded Projects

Swarm Based Algorithms for Anomalous Rules Discovery in Temporal Data Streams The RM81,000 project funded by the Fundamental Research Grant Scheme, Ministry of Education Malaysia aims to develop various swarm based algorithms in handling large big and real time temporal data streams.

Advanced Nature Inspired Computing for Spatio-Temporal Climate Change Predictive Analytics. The RM200,000 Arus Perdana project funded by the Research University Grant Scheme, Ministry of Education Malaysia aims to develop danger theory algorithms specifically to detect abrupt changes in climate data.

Climate Modeling using Nature-based Hybrid Optimization Approach for Time-series Prediction. The RM54,000 project funded by the Exploratory Research Grant Scheme, Ministry of Education Malaysia aims to develop nature based optimization algorithms in handling large big and real time temporal data streams.

Danger Theory Based Algorithms for Abrupt Change Detection The RM109,000 project funded by the Fundamental Research Grant Scheme, Ministry of Education Malaysia aims to develop danger theory algorithms specifically to detect abrupt changes in climate data.

Nature Inspired Text Based Structure Modelling Approach for Signature Detection in Weather Prediction Problem

The RM80,000 project funded by the Exploratory Research Grant Scheme, Ministry of Education Malaysia aims to propose a new way of representing climate time series data for rainfall signature detection in order to predict weather in Malaysia

A Symbolic Representation Approach for Uncertain Time Series Data Reduction

The RM80,000 project funded by the Fundamental Research Grant Scheme, Ministry of Education Malaysia aims to propose a new way of representing climate time series data to handle and detect uncertainties in data

OUR PUBLICATIONS

1. Najmeh Sadat Jaddi, Salwani Abdullah, Abdul Razak Hamdan. 2015. Multipopulation algorithm based optimization of artificial neural

network model. Information Sciences 294 (2015) 628–644. Elsevier.

2. Vahid Beiranvand, Mohamad Mobasher Kashani, Azuraliza Abu Bakar. 2014.Multi-objective PSO Algorithm for Mining Numerical

Association Rules without a Priori Discretization. Expert Systems with Applications 41 (2014) 4259–4273.

3. Nabilah Filzah Mohd. Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan Representing data without lost

compression properties in time series: a review. World Academy of Science, Engineering and Technology. 84:2000-2003

4. Azuraliza Abu Bakar ,Almahdi Mohammed Ahmed, Abdul Razak Hamdan. Discretization of Time Series Dataset Using Relative

Frequency and K-Nearest Neighbor Approach. Lecture Notes in Artificial Intelligence, 2010, Volume 6440, Advanced Data Mining

and Applications, Pages 193-201. ISBN 978-3-642-17315-8. Springer Verlag, Berlin.

5. Almahdi Mohammed Ahmed, Azuraliza Abu Bakar, Abdul Razak Hamdan, Sharifah Mastura Syed Abdullah, Othman Jaafar. 2012.

Harmony Search Algorithm for Optimal Word Size in Symbolic Time Series Representation, 4th Conference on Data Mining and

Optimization (DMO2012). MALAYSIA.

6. Almahdi Mohammed Ahmed, Azuraliza Abu Bakar, Abdul Razak Hamdan. 2011. Harmony Search Algorithm for Optimal Word Size in

Symbolic Time Series Representation, 3rd Conference on Data Mining and Optimization (DMO2011). Malaysia. pp 57-62

7. Siti Nur Kamaliah Kamarudin, Azuraliza Abu Bakar. 2013. Neural Network Algorithm Variants for Malaysian Weather Prediction. 2nd

Multi Conference of Artificial Intelligence Technology (MCAIT-2013). 29-30 August. Shah Alam. MALAYSIA.

8. Ghassan Saleh Al-Dharhani. 2014. Temporal Frequent Patterns Mining In Climate Change. 3rd Doctoral Seminar on Artificial

Intelligence Technology. November 2014, MALAYSIA

9. Yahia Mohammed Benyahmed. 2014. Time-Weighted Average Approach For Symbolic Representation Of Time Series Data. 3rd

Doctoral Seminar on Artificial Intelligence Technology. November 2014, MALAYSIA

10. Shakirah Mohd Taib. 2014. Bag-Of-Words Representation For Weather Time Series Classification. 3rd Doctoral Seminar on Artificial

Intelligence Technology. November 2014, MALAYSIA.

11. Maryam Mousavi. 2014. Bio-Inspired Density Based Algorithm For Data Stream Clustering. 3rd Doctoral Seminar on Artificial

Intelligence Technology. November 2014, MALAYSIA.

12. Almahdi Mohammed Ahmed, Azuraliza Abu Bakar, Abdul Razak Hamdan, Sharifah Mastura Syed Abdullah, Othman Jaafar. 2014.

Sequential Pattern Discovery Algorithm for Malaysia Rainfall Prediction. International Conference on Computational and

Experimental Science and Engineering. 25-29 Oktober. Turki.

13. Ghassan Al-Dharhani, Zulaiha Othman, Azuraliza Bakar, Sharifah Mastura Syed Abdullah. 2014. Enhanced RBFN backpropagation for

multi-orientation weather pattern prediction. International Conference on Computational and Experimental Science and

Engineering. 25-29 Oktober. Turki.