From Data Modeling to Algorithmic Modeling in the Big Data Era: Water Resources Security in the Asia-Pacific Region under Conditions of Climate Change

Advances in computing technologies allow machine learning algorithms to automatically, repeatedly and quickly apply complex mathematical calculations to water resources and environmental security challenges. The concomitant increase in “big data” research, development, and applications is also driving the popularity of real-time automated model building and data mining for these security problems under conditions of climate change. The last decade has seen considerable growth in the theory and application in Artificial Intelligence (AI). It is shown that machine learning, a subset of AI, constitutes a data analysis method that focuses on the development of algorithms that can iteratively learn from data to uncover previously “hidden insights” for environmental security managers in the Asia Pacific. It is concluded that deep machine learning (i.e. deep learning) can help to reduce losses to ecosystems, livelihoods, and businesses. In particular, these losses can be more likely prevented and minimized through the use of data and algorithmic modeling that improves community resilience by institutionalizing sustainable hazard mitigation within accepted processes of water resources community planning and economic development before disasters happen. Key environmental threats including foods, population extinction, water quality and climate change are considered. The difference between the algorithmic modeling and data modeling cultures are summarized with reference to the schools in which they originate, the assumptions they work on, the type of data they deal with, and the techniques used.