Predictive water condition monitoring

Patent metadata

Field	Value
Publication number	US-2022018824-A1
Application number	US-202016933322-A
Country	US
Kind code	A1
Filing date	Jul 20, 2020
Priority date	Jul 20, 2020
Publication date	Jan 20, 2022
Grant date	—

Abstract

Techniques for predictive water condition monitoring are described herein. An aspect includes a method that includes monitoring, by one or more processors, at least one water sensor to establish a baseline of a water condition model and monitoring one or more water conditions. A predicted water condition is determined based on the water condition model and the one or more water conditions. An alert is transmitted to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.

First claim

What is claimed is:

1 . A method comprising:
monitoring, by one or more processors, at least one water sensor to establish a baseline of a water condition model; monitoring one or more water conditions; determining a predicted water condition based on the water condition model and the one or more water conditions; and transmitting an alert to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.

2 . The method of claim 1 , further comprising:
monitoring a water sensor network comprising a plurality of water sensors at one or more known locations; and tracking variations in the one or more water conditions with respect to the one or more known locations.

3 . The method of claim 2 , wherein the water sensor network is distributed between a plurality of public and private water distribution locations.

4 . The method of claim 2 , wherein the water sensor network is associated with one or more wells accessing an aquifer.

5 . The method of claim 2 , further comprising:
identifying a probable location of introduction of a contaminant based on a rate of change of the one or more water conditions relative to the known locations of the water sensors; and generating a notification of the probable location and the one or more water conditions.

6 . The method of claim 1 , further comprising:
applying machine learning to train the water condition model to dynamically determine one or more characteristics associated with the one or more water conditions.

7 . The method of claim 1 , further comprising:
accessing supplemental data comprising one or more of a weather data source and an environmental data source; applying machine learning to train the water condition model based on one or more correlations between the one or more water conditions and the supplemental data; accessing forecast data comprising one or more of weather forecast data and environmental forecast data; and predicting the water condition based on the water condition model and the forecast data.

8 . A system comprising:
a memory having computer readable instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions controlling the one or more processors to perform operations comprising:
monitoring at least one water sensor to establish a baseline of a water condition model;
monitoring one or more water conditions;
determining a predicted water condition based on the water condition model and the one or more water conditions; and
transmitting an alert to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.

9 . The system of claim 8 , wherein the computer readable instructions are configured to control the one or more processors to perform operations comprising:
monitoring a water sensor network comprising a plurality of water sensors at one or more known locations; and tracking variations in the one or more water conditions with respect to the one or more known locations.

10 . The system of claim 9 , wherein the water sensor network is distributed between a plurality of public and private water distribution locations.

11 . The system of claim 9 , wherein the water sensor network is associated with one or more wells accessing an aquifer.

12 . The system of claim 9 , wherein the computer readable instructions are configured to control the one or more processors to perform operations comprising:
identifying a probable location of introduction of a contaminant based on a rate of change of the one or more water conditions relative to the known locations of the water sensors; and generating a notification of the probable location and the one or more water conditions.

13 . The system of claim 8 , wherein the computer readable instructions are configured to control the one or more processors to perform operations comprising:
applying machine learning to train the water condition model to dynamically determine one or more characteristics associated with the one or more water conditions.

14 . The system of claim 8 , wherein the computer readable instructions are configured to control the one or more processors to perform operations comprising:
accessing supplemental data comprising one or more of a weather data source and an environmental data source; applying machine learning to train the water condition model based on one or more correlations between the one or more water conditions and the supplemental data; accessing forecast data comprising one or more of weather forecast data and environmental forecast data; and predicting the water condition based on the water condition model and the forecast data.

15 . A computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by one or more processors to cause the one or more processors to perform operations comprising:
monitoring at least one water sensor to establish a baseline of a water condition model; monitoring one or more water conditions; determining a predicted water condition based on the water condition model and the one or more water conditions; and transmitting an alert to one or more devices based on determining that the predicted water condition indicates a predicted contaminant level above a threshold.

16 . The computer program product of claim 15 , wherein the program instructions control the one or more processors to perform operations comprising:
monitoring a water sensor network comprising a plurality of water sensors at one or more known locations; and tracking variations in the one or more water conditions with respect to the one or more known locations.

17 . The computer program product of claim 16 , wherein the program instructions control the one or more processors to perform operations comprising:
identifying a probable location of introduction of a contaminant based on a rate of change of the one or more water conditions relative to the known locations of the water sensors; and generating a notification of the probable location and the one or more water conditions.

18 . The computer program product of claim 15 , wherein the program instructions control the one or more processors to perform operations comprising:
applying machine learning to train the water condition model to dynamically determine one or more characteristics associated with the one or more water conditions.

19 . The computer program product of claim 18 , wherein the program instructions control the one or more processors to perform operations comprising:
accessing supplemental data comprising one or more of a weather data source and an environmental data source; and applying machine learning to train the water condition model based on one or more correlations between the one or more water conditions and the supplemental data.

20 . The computer program product of claim 19 , wherein the program instructions control the one or more processors to perform operations comprising:
accessing forecast data comprising one or more of weather forecast data and environmental forecast data; and predicting the water condition based on the water condition model and the forecast data.

Assignees

• IBM

Inventors

• Hicks Andrew C M
• Conti Thomas William
• Stamboni Diane Marie
• Mikijanic Christine D
• Brooker Christopher G
• Evans Andrew

Classifications

• G06N5/048

  Fuzzy inferencing · CPC title

• G06N20/00

  Machine learning · CPC title

• G01W1/10

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G01N33/18 Primary

  Water · CPC title

• G06N5/04

  Inference or reasoning models · CPC title