Drought forecasting unlocked by AI
Ground-breaking research conducted by James Cook University will soon give farmers and water infrastructure managers the ability to better prepare for future droughts, thanks to artificial intelligence (AI).
A successful trial conducted by Civil Engineering Senior Lecturer Dr Bithin Datta and his team of Honours students used an AI-based tool known as the Artificial Neural Network (ANN) to analyse existing hydrological and meteorological data related to the Ross River basin area in Townsville.
The team were seeking to find patterns that may correspond to an impending drought scenario.
“One of the aims of the work we carried out was to forecast drought conditions with a lead time of three months to six months, as well as to predict future possible ranges of the Ross River Dam level, groundwater levels and groundwater salinity,” Dr Datta said.
“This new approach seems to perform very well especially in the urban water system setting.”
ANNs are known for their exceptional pattern recognition capabilities, with the research team using existing hydrological parameter values, such as reservoir capacity and streamflow, soil moisture, and climatic conditions such as sea surface temperature and atmospheric pressures, to predict dam storage levels, groundwater levels and salinity in groundwater several months in advance.
“The ANN mimics the intricate recognition and decision-making process in human brains,” Dr Datta said.
A successful trial conducted by Civil Engineering Senior Lecturer Dr Bithin Datta and his team of Honours students used an AI-based tool known as the Artificial Neural Network (ANN) to analyse existing hydrological and meteorological data related to the Ross River basin area in Townsville.
The team were seeking to find patterns that may correspond to an impending drought scenario.
“One of the aims of the work we carried out was to forecast drought conditions with a lead time of three months to six months, as well as to predict future possible ranges of the Ross River Dam level, groundwater levels and groundwater salinity,” Dr Datta said.
“This new approach seems to perform very well especially in the urban water system setting.”
ANNs are known for their exceptional pattern recognition capabilities, with the research team using existing hydrological parameter values, such as reservoir capacity and streamflow, soil moisture, and climatic conditions such as sea surface temperature and atmospheric pressures, to predict dam storage levels, groundwater levels and salinity in groundwater several months in advance.
“The ANN mimics the intricate recognition and decision-making process in human brains,” Dr Datta said.
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