Artificial-intelligence-based prediction of storage capacities in water reservoirs

A system comprising:

• at least one hardware processor;
• and
• one or more software modules that are configured to, when executed by the at least one hardware processor, during a training phase,
• acquire a training dataset comprising a plurality of labeled feature vectors, wherein each of the plurality of labeled feature vectors comprises a time series of a plurality of tuples, wherein each of the plurality of tuples comprises a value for each of one or more climate parameters and one or more reservoir parameters for a respective water reservoir, and wherein each of the plurality of labeled feature vectors is labeled with a target value of at least one storage parameter of the respective water reservoir, and
• use the training dataset to train a machine-learning model to predict a value of the at least one storage parameter for any of a plurality of water reservoirs,
• wherein the machine-learning model comprises a recurrent neural network with long short-term memory (LSTM).

A non-transitory computer-readable medium having instructions stored therein, wherein the instructions, when executed by a processor, cause the processor to, during a training phase:

• acquire a training dataset comprising a plurality of labeled feature vectors, wherein each of the plurality of labeled feature vectors comprises a time series of a plurality of tuples, wherein each of the plurality of tuples comprises a value for each of one or more climate parameters and one or more reservoir parameters for a respective water reservoir, and wherein each of the plurality of labeled feature vectors is labeled with a target value of at least one storage parameter of the respective water reservoir;
• and
• use the training dataset to train a machine-learning model to predict a value of the at least one storage parameter for any of a plurality of water reservoirs,
• wherein the machine-learning model comprises a recurrent neural network with long short-term memory (LSTM).