Pradeep Hewage

  • MSc Computing, Edge Hill University
  • PGD in IT, The Association of Business Practitioners
  • Professional Graduate Diploma, British Computer Society (BCS)
  • BSc Physics and Mathematics, University of Colombo, Sri Lanka
Pradeep Hewage 

Research Contact Teaching

Climate modelling, Machine Learning, Big Data, Internet of Things (IoT), Artificial Neural Networks, Domain Specific Languages

Pradeep Hewage
Department of Computer Science
Edge Hill University
L39 4QP

Phone: 01695 575171 ext. 1872
Office: THF09

  • Digital World 1: Computer Architecture
  • Digital World 2: System Analysis and Design
  • Object Oriented programming in Java


Pradeep is a Doctoral Tutor currently working towards his PhD at the Department of Computer Science at Edgehill University. He is an ISTQB qualified software tester. Before joining Edge Hill, he worked five years as a Software Tester in the UK’s leading computer firms. Prior to that, he worked as a full-time Assistant Lecturer in a leading computer institution in Sri Lanka. He is a member of the prestigious British Computer Society (MBCS).

Pradeep’s PhD thesis is titled “Understanding the impact of introducing climate modelling to a community of users”. This research aims to configure Weather Research and Forecasting (WRF) scientific model’s local weather prediction based on other external parameters and the historical data to obtain an operational, costumes, and more accurate weather prediction system for a community of users in a specific geographical area. This research focuses on the agricultural sector and farmers as the community of users, but the same concept can be extended to other sectors with the knowledge of selecting appropriate weather parameters to configure the WRF. Non-predictive or inaccurate weather forecasting can severely impact the farmer’s ability to engage in activities like ploughing, cultivation, and others; resulting in a direct impact on the resources that a farmer requires to carry out these operations. This research requires the combination of the predictive data from WRF output, historical data, physics data from sensor devices which are located across field systems, and farm data to get an intelligent and reactive output for an award-winning precision farming service provider. These data will be collated and used to train a neural network to identify emergent patterns for the purposes of making future predictions.

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