SYSTEM FOR DETECTION AND PREDICTION OF WATER QUALITY EVENTS

Abstract

A method of evaluating a water sample for the presence or possible future presence of nitrification comprises obtaining data values of a number of parameters, processing the data values to determine correlation coefficients, to identify any linear dependencies, to standardize the scales, evaluating the data values over a plurality of proliferation time periods and neuron numbers, calculating MSEs and R2's from the evaluations, and estimating a valid likelihood of nitrification of the water sample. A method of evaluating a water sample for the presence or possible future presence of nitrification, comprises obtaining data values of a number of parameters, statistically pre-processing the data values and supplying the pre-processed data values to a neural network. Apparatus, media and processors which are used in performing such methods.

Description

BRIEF DESCRIPTION OF THE FIGURES

A full understanding of the invention can be gained from the following detailed description of the invention when read in conjunction with the accompanying figures in which:

FIG. 1 is a block diagram of a preferred embodiment of an apparatus according to the present invention.

FIG. 2 is a flow chart showing Phase I of neural network modeling, that is, the generation of the model. More specifically, it shows how the procured data is preprocessed, how the neural network is modeled, how the neural network iteratively trains itself to find the optimal weights, how it verifies the model, and finally, how it stores the appropriate problem-specific neural network.

FIG. 3 is a flow chart showing Phase II of neural network modeling, that is, the application of the model. This phase takes the saved neural network from the previous phase and uses procured data that is validated to detect and predict water quality conditions. Data validation encompasses checking the data for insufficiency, checking the data for abnormality, checking whether the data is historically justified and filtering random sensor noise.

FIG. 4 is a table showing the correlation coefficients calculated between various water quality parameters in an example of nitrification prediction.

FIG. 5 are two graphs, the first of which is the actual water quality data plot for pH of a water sample over a period of time (top) and the second of which is the standardized water quality data plot for pH of the water sample over the same period of time (bottom).

FIG. 6 includes two graphs, the first of which is the actual water quality data plot for temperature of a water sample over a period of time (top) and the second of which is the standardized water quality data plot for temperature of the water sample over the same period of time (bottom).

FIG. 7 is a graph of the mean squared errors (MSE) that were calculated with two neurons and ten trials for five different time delay periods in the nitrification example.

FIG. 8 is a graph of the squared correlation coefficients (R²) that were calculated with two neurons and ten trials for five different time delay periods in the nitrification example.

FIG. 9 is a graph of the MSE calculations with three neurons and ten trials for five different time delay periods in the nitrification example.

FIG. 10 is a graph of the R² calculations with three neurons and ten trials for five different time delay periods in the nitrification example.

FIG. 11 is a schematic diagram of the architecture of the neural network model.

FIG. 12 is a graph showing the error function minimization. The blue line represents the training set, the green line represents the cross validation set, and the black line represents the ideal value for MSE that the model wants to achieve.

FIG. 13 is a graph showing how the neural network model is generated by separating the data into three data sets: training, cross validation, and testing sets.

FIG. 14 is a graph showing plots of actual data and filtered data.

Claims

1. A method of evaluating a water sample for the possible future presence of an algal bloom, comprising: obtaining data values of pH of a water sample from a pH sensor over a period of time;obtaining data values of turbidity of said water sample from a turbidity sensor over a period of time;obtaining data values of conductivity of said water sample from a conductivity sensor over a period of time;obtaining data values of temperature of said water sample from a temperature sensor over a period of time;obtaining data values of dissolved oxygen of said water sample from a dissolved oxygen sensor over a period of time;obtaining data values of total organic carbon of said water sample from a total organic carbon sensor over a period of time;obtaining data values of phycocyanin of a water sample from a phycocyanin sensor over a period of time;obtaining data values of chlorophyll of said water sample from a chlorophyll sensor over a period of time;processing said data values to determine correlation coefficients between data parameters;processing said data values to identify any linear dependencies between data parameters;processing said data values to standardize the scales of said data values;evaluating said data values over a plurality of proliferation time periods and neuron numbers;calculating MSEs and R2's from said evaluations;determining an optimal proliferation time period and optimal neuron number using said calculations;determining whether any of the said data values are abnormal; and, in the event they all are not,estimating a valid likelihood that said water sample will be subject to an algal bloom at one or more specific times in the future, based on subsequent, periodic data values and said proliferation time period and neuron number.

2. A method as recited in claim 1, wherein said data values are obtained from a sequence of successive water samples taken from a body of water.

3. A method as recited in claim 1, further comprising activating a visible and/or audible alarm if a predicted value exceeds a corresponding threshold value or if a specific water quality condition is predicted.

4. A method as recited in claim 1, wherein said method is computer-implemented.

5. A method as recited in claim 1, wherein said method further comprises filtering at least some of said data values.

6. A method of evaluating a water sample for the possible future presence of MIB and/or geosmin, comprising: obtaining data values of pH of a water sample from a pH sensor over a period of time;obtaining data values of turbidity of said water sample from a turbidity sensor over a period of time;obtaining data values of conductivity of said water sample from a conductivity sensor over a period of time;obtaining data values of temperature of said water sample from a temperature sensor over a period of time;obtaining data values of dissolved oxygen of said water sample from a dissolved oxygen sensor over a period of time;obtaining data values of total organic carbon of said water sample from a total organic carbon sensor over a period of time;obtaining data values of phycocyanin of a water sample from a phycocyanin sensor over a period of time;obtaining data values of chlorophyll of said water sample from a chlorophyll sensor over a period of time;processing said data values to determine correlation coefficients between data parameters;processing said data values to identify any linear dependencies between data parameters;processing said data values to standardize the scales of said data values;evaluating said data values over a plurality of proliferation time periods and neuron numbers;calculating MSEs and R2's from said evaluations;determining an optimal proliferation time period and optimal neuron number using said calculations;determining whether any of the said data values are abnormal; and, in the event they all are not,estimating a valid likelihood that said water sample will be subject to MIB and/or geosmin at one or more specific times in the future, based on subsequent, periodic data values and said proliferation time period and neuron number.

7. A method as recited in claim 6, wherein said data values are obtained from a sequence of successive water samples taken from a body of water.

8. A method as recited in claim 6, further comprising activating a visible and/or audible alarm if a predicted value exceeds a corresponding threshold value or if a specific water quality condition is predicted.

9. A method as recited in claim 6, wherein said method is computer-implemented.

10. A method as recited in claim 6, wherein said method further comprises filtering at least some of said data values.

11. A method of evaluating a water sample for the presence or possible future presence of nitrification, comprising: obtaining data values of pH of a water sample from a pH sensor over a period of time;obtaining data values of turbidity of said water sample from a turbidity sensor over a period of time;obtaining data values of conductivity of said water sample from a conductivity sensor over a period of time;obtaining data values of temperature of said water sample from a temperature sensor over a period of time;obtaining data values of dissolved oxygen of said water sample from a dissolved oxygen sensor over a period of time;obtaining data values of total organic carbon of said water sample from a total organic carbon sensor over a period of time;obtaining data values of ammonia of said water sample from an ammonia sensor over a period of time;obtaining data values of total chlorine of said water sample from a chlorine sensor over a period of time;obtaining data values of nitrite of said water sample from a nitrite sensor over a period of time;processing said data values to determine correlation coefficients between data parameters;processing said data values to identify any linear dependencies between data parameters;processing said data values to standardize the scales of said data values;evaluating said data values over a plurality of proliferation time periods and neuron numbers;calculating MSEs and R2's from said evaluations;determining an optimal proliferation time period and optimal neuron number using said calculations;determining whether any of the said data values are abnormal; and, in the event they all are not,estimating a valid likelihood that said water sample is subject to or will be subject to nitrification at one or more specific times in the future, based on subsequent, periodic data values and said proliferation time period and neuron number.

12. A method as recited in claim 11, wherein said data values are obtained from a sequence of successive water samples taken from a body of water.

13. A method as recited in claim 11, further comprising activating a visible and/or audible alarm if a predicted value exceeds a corresponding threshold value or if a specific water quality condition is predicted.

14. A method as recited in claim 11, wherein said method is computer-implemented.

15. A method as recited in claim 11, wherein said method further comprises filtering at least some of said data values.

16. A method of evaluating a water sample for the possible future presence of TTHM and/or HAA5, comprising: obtaining data values of pH of a water sample from a pH sensor over a period of time;obtaining data values of turbidity of said water sample from a turbidity sensor over a period of time;obtaining data values of conductivity of said water sample from a conductivity sensor over a period of time;obtaining data values of temperature of said water sample from a temperature sensor over a period of time;obtaining data values of dissolved oxygen of said water sample from a dissolved oxygen sensor over a period of time;obtaining data values of total organic carbon of said water sample from a total organic carbon sensor over a period of time;obtaining data values of ammonia of said water sample from an ammonia sensor over a period of time;obtaining data values of free chlorine of said water sample from a chlorine sensor over a period of time;obtaining data values of any interested DBP of said water sample from a DBP sensor over a period of time;processing said data values to determine correlation coefficients between data parameters;processing said data values to identify any linear dependencies between data parameters;processing said data values to standardize the scales of said data values;evaluating said data values over a plurality of proliferation time periods and neuron numbers;calculating MSEs and R2's from said evaluations;determining an optimal proliferation time period and optimal neuron number using said calculations;determining whether any of the said data values are abnormal; and, in the event they all are not,estimating a valid likelihood that said water sample will be subject to TTHM and/or HAA5 at one or more specific times in the future, based on subsequent, periodic data values and said proliferation time period and neuron number.

17. A method as recited in claim 16, wherein said data values are obtained from a sequence of successive water samples taken from a body of water.

18. A method as recited in claim 16, further comprising activating a visible and/or audible alarm if a predicted value exceeds a corresponding threshold value or if a specific water quality condition is predicted.

19. A method as recited in claim 16, wherein said method is computer-implemented.

20. A method as recited in claim 16, wherein said method further comprises filtering at least some of said data values.

21. A method of evaluating a water sample for the presence or possible future presence of nitrification, comprising: obtaining data values of pH of a water sample from a pH sensor over a period of time;obtaining data values of turbidity of said water sample from a turbidity sensor over a period of time;obtaining data values of conductivity of said water sample from a conductivity sensor over a period of time;obtaining data values of temperature of said water sample from a temperature sensor over a period of time;obtaining data values of dissolved oxygen of said water sample from a dissolved oxygen sensor over a period of time;obtaining data values of total organic carbon of said water sample from a total organic carbon sensor over a period of time;obtaining data values of ammonia of said water sample from an ammonia sensor over a period of time;obtaining data values of total chlorine of said water sample from a chlorine sensor over a period of time;obtaining data values of nitrite of said water sample from a nitrite sensor over a period of time;statistically pre-processing said data values to obtain pre-processed data values; andsupplying said preprocessed data values to a neural network.

22. A method as recited in claim 21, wherein said data values are obtained from a sequence of successive water samples taken from a body of water.

23. A method as recited in claim 21, further comprising activating a visible and/or audible alarm if a predicted value exceeds a corresponding threshold value or if a specific water quality condition is predicted.

24. A method as recited in claim 21, wherein said method is computer-implemented.

25. A method as recited in claim 21, wherein said method further comprises filtering at least some of said data values.

26. A method as recited in claim 21, wherein said neural network comprises a Levenberg-Marquardt optimization algorithm.

27. A method as recited in claim 21, wherein said neural network comprises from 2 to 15 neurons.

28. An apparatus comprising: at least one sensor;a data evaluation component which receives sensor data from said sensor and statistically pre-processes said sensor data to produce pre-processed data values; anda neural network to which said pre-processed data values are supplied.

29. A computer-readable medium having computer-executable components, comprising: means for statistically pre-processing data values to obtain pre-processed data values; anda neural network to which said pre-processed data values are supplied.

30. A computer-readable medium comprising computer instructions which, when executed by a computer, perform a method as recited in claim 1.

31. A computer-readable medium comprising computer instructions which, when executed by a computer, perform a method as recited in claim 6.

32. A computer-readable medium comprising computer instructions which, when executed by a computer, perform a method as recited in claim 11.

33. A computer-readable medium comprising computer instructions which, when executed by a computer, perform a method as recited in claim 16.

34. A computer-readable medium comprising computer instructions which, when executed by a computer, perform a method as recited in claim 21.

35. A processor on which is stored software for carrying out a method as recited in claim 1.

36. A processor on which is stored software for carrying out a method as recited in claim 6.

37. A processor on which is stored software for carrying out a method as recited in claim 11.

38. A processor on which is stored software for carrying out a method as recited in claim 16.

39. A processor on which is stored software for carrying out a method as recited in claim 21.

40. An apparatus comprising a computer-readable medium as recited in claim 29, further comprising at least one alarm which is activated when an emergency is detected or predicted.