SYSTEM AND METHOD FOR PREDICTING AND INDEXING WATER QUALITY LEVELS

1 .- 20 . (canceled)

21 . A method comprising:

receiving, at a computer system, hydrology data for a predefined geographic region;

receiving, at the computer system, real-time sensor data associated with water quality for the predefined geographic region;

executing, via at least one processor of the computer system using the hydrology data and the real-time sensor data, an artificial intelligence (AI) algorithm configured to generate a transport-prediction model which simulates overland and riverine movement of contaminants toward a downstream position of interest,

wherein output of the transport-prediction model comprises a contaminant arrival time and contaminant load at the downstream position of interest;

generating, via the at least one processor, an event-trigger indicator when the contaminant load exceeds a threshold derived from at least one contaminant class, health factor, or regulatory limit; and

transmitting the event-trigger indicator to a remote device configured to initiate a mitigation or alert action.

22 . The method of claim 21 , wherein the AI algorithm, when executed, further uses at least one of precipitation forecasts for the predefined geographic region, hydrologic topology for the predefined geographic region, and historical contaminant patterns for the predefined geographic region.

23 . The method of claim 21 , wherein generating the transport-prediction model comprises integrating runoff, travel schema, and progressive contaminant aggregation parameters derived from a water pedigree for the predefined geographic region.

24 . The method of claim 21 , wherein the AI algorithm computes a confidence score associated with the contaminant arrival time by evaluating variance across multiple prediction algorithms comprising a storm-effects algorithm, a hydrology prediction algorithm, and an overland and riverine transport algorithm.

25 . The method of claim 21 , further comprising:

retraining the AI algorithm using feedback data representing at least one of realized storm effects, realized contaminant transport, or realized contaminant concentrations, thereby modifying one or more prediction parameters of the AI algorithm for future iterations.

26 . The method of claim 21 , wherein the event-trigger indicator comprises a tiered severity classification selected by the AI algorithm based on at least one of predicted contaminant toxicity, predicted exposure conditions, or predicted duration of impact.

27 . The method of claim 21 , further comprising:

identifying, via the AI algorithm, a contributing upstream source for the contaminant load based on sector descriptions describing upstream geographic regions, the sector descriptions providing at least one of agricultural, industrial, stormwater, wastewater, or other contributing activities.

28 . The method of claim 21 , wherein the AI algorithm:

simulates multiple contaminant-transport scenarios using temporally-shifted precipitation forecasts; and

outputs a range of contaminant arrival times and contaminant loads, resulting in simulated outputs,

wherein the event-trigger indicator is based on a worst-case scenario among the simulated outputs.

29 . A system comprising:

at least one processor; and

a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

receiving hydrology data for a predefined geographic region;

receiving real-time sensor data associated with water quality for the predefined geographic region;

executing, using the hydrology data and the real-time sensor data, an artificial intelligence (AI) algorithm configured to generate a transport-prediction model which simulates overland and riverine movement of contaminants toward a downstream position of interest,

wherein output of the transport-prediction model comprises a contaminant arrival time and contaminant load at the downstream position of interest;

generating an event-trigger indicator when the contaminant load exceeds a threshold derived from at least one contaminant class, health factor, or regulatory limit; and

transmitting the event-trigger indicator to a remote device configured to initiate a mitigation or alert action.

30 . The system of claim 29 , wherein the AI algorithm, when executed, further uses at least one of precipitation forecasts for the predefined geographic region, hydrologic topology for the predefined geographic region, and historical contaminant patterns for the predefined geographic region.

31 . The system of claim 29 , wherein generating the transport-prediction model comprises integrating runoff, travel schema, and progressive contaminant aggregation parameters derived from a water pedigree for the predefined geographic region.

32 . The system of claim 29 , wherein the AI algorithm computes a confidence score associated with the contaminant arrival time by evaluating variance across multiple prediction algorithms comprising a storm-effects algorithm, a hydrology prediction algorithm, and an overland and riverine transport algorithm.

33 . The system of claim 29 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

retraining the AI algorithm using feedback data representing at least one of realized storm effects, realized contaminant transport, or realized contaminant concentrations, thereby modifying one or more prediction parameters of the AI algorithm for future iterations.

34 . The system of claim 29 , wherein the event-trigger indicator comprises a tiered severity classification selected by the AI algorithm based on at least one of predicted contaminant toxicity, predicted exposure conditions, or predicted duration of impact.

35 . The system of claim 29 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

identifying, via the AI algorithm, a contributing upstream source for the contaminant load based on sector descriptions describing upstream geographic regions, the sector descriptions providing at least one of agricultural, industrial, stormwater, wastewater, or other contributing activities.

36 . The system of claim 29 , wherein the AI algorithm:

simulates multiple contaminant-transport scenarios using temporally-shifted precipitation forecasts; and

outputs a range of contaminant arrival times and contaminant loads, resulting in simulated outputs,

wherein the event-trigger indicator is based on a worst-case scenario among the simulated outputs.

37 . A non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

receiving hydrology data for a predefined geographic region;

receiving real-time sensor data associated with water quality for the predefined geographic region;

executing, using the hydrology data and the real-time sensor data, an artificial intelligence (AI) algorithm configured to generate a transport-prediction model which simulates overland and riverine movement of contaminants toward a downstream position of interest,

wherein output of the transport-prediction model comprises a contaminant arrival time and contaminant load at the downstream position of interest;

generating an event-trigger indicator when the contaminant load exceeds a threshold derived from at least one contaminant class, health factor, or regulatory limit; and

transmitting the event-trigger indicator to a remote device configured to initiate a mitigation or alert action.

38 . The non-transitory computer-readable storage medium of claim 37 , wherein the AI algorithm, when executed, further uses at least one of precipitation forecasts for the predefined geographic region, hydrologic topology for the predefined geographic region, and historical contaminant patterns for the predefined geographic region.

39 . The non-transitory computer-readable storage medium of claim 37 , wherein generating the transport-prediction model comprises integrating runoff, travel schema, and progressive contaminant aggregation parameters derived from a water pedigree for the predefined geographic region.

40 . The non-transitory computer-readable storage medium of claim 37 , wherein the AI algorithm computes a confidence score associated with the contaminant arrival time by evaluating variance across multiple prediction algorithms comprising a storm-effects algorithm, a hydrology prediction algorithm, and an overland and riverine transport algorithm.