Prediction of water parameters based on multivariate regression trees estimation

A system for creating machine learning (ML) based regression trees trained for estimating a level of at least one water parameter, comprising:

• at least one processor configured to execute a code, the code comprising:
• code instructions to use a plurality of training samples relating to a plurality of water samples to create a regression tree comprising a plurality of nodes arranged in a plurality of branches extending from a root node to a plurality of leaf nodes by splitting each of the plurality of nodes into at least two respective child nodes by:
• splitting a set of the plurality of training samples which propagated to the respective node into at least two subsets based on each of a plurality of candidate split values applied for each of at least one of a plurality of physical features of a plurality of the training samples,
• training two ML models each associated with one of the child nodes, each ML mode is trained using a plurality of spectral features of the propagated samples of a respective one of the two subsets, and
• selecting an optimal split value from the plurality of candidate split values by minimizing loss of each of the trained ML models applied to estimate a level of at least one water parameter in the propagated samples of the respective subset;
• and
• code instructions to output the regression tree for estimating the level of the at least one water parameter in at least one new water sample.

A method of estimating a level of at least one water parameter using trained machine learning (ML) based regression trees, comprising:

• using at least one processor for:
• receiving a plurality of physical features and a plurality of spectral features or, in cases with no spectral features, ORP, pH and UV values relating to at least one sample of water,
• propagating the at least one sample of water through a regression tree comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of at least one water parameter in water samples;
• identifying a certain leaf node of the plurality of leaf nodes to which the at least one water sample propagated based on the plurality of physical features;
• estimating a level of the at least one water parameter in the at least one water sample by applying the ML model associated with the certain leaf node to the plurality of spectral features;
• and
• initiating at least one action based on the estimated level of the at least one water parameter.

A system for estimating a level of at least one water parameter using trained machine learning (ML) based regression trees, comprising:

• at least one processor configured to execute a code, the code comprising:
• code instructions to receive a plurality of physical features and a plurality of spectral features relating to at least one sample of water;
• code instructions to propagate the at least one sample of water through a regression tree comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of at least one water parameter in water samples;
• code instructions to identify a certain leaf node of the plurality of leaf nodes to which the at least one water sample propagated based on the plurality of physical features;
• code instructions to estimate a level of the at least one water parameter in the at least one water sample by applying the ML model associated with the certain leaf node to the plurality of spectral features;
• and
• code instructions to initiate at least one action based on the estimated level of the at least one water parameter.