System for detection and prediction of water quality events

A method of evaluating a water sample for the possible future presence of MIB 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 said 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 with a computer said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of phycocyanin, said data values of chlorophyll to determine correlation coefficients between data parameters;
• processing said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of phycocyanin, said data values of chlorophyll to identify any linear dependencies between data parameters;
• processing said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of phycocyanin, said data values of chlorophyll to standardize the scales of said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of phycocyanin, said data values of chlorophyll;
• evaluating said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of phycocyanin, said data values of chlorophyll 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 of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of phycocyanin, said data values of chlorophyll are abnormal;
• and, in the event they all are not,
• estimating a valid likelihood that said water sample will be subject to MIB 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.

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 with a computer said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of total chlorine, said data values of nitrite to determine correlation coefficients between data parameters;
• processing said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of total chlorine, said data values of nitrite to identify any linear dependencies between data parameters;
• processing said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of total chlorine, said data values of nitrite to standardize the scales of said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of total chlorine, said data values of nitrite;
• evaluating said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of total chlorine, said data values of nitrite 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 of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of total chlorine, said data values of nitrite 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.

A method of evaluating a water sample for the possible future presence of TTHM 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 DBP of said water sample from a DBP sensor over a period of time;
• processing with a computer said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of free chlorine, said data values of any DBP to determine correlation coefficients between data parameters;
• processing said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of free chlorine, said data values of any DBP to identify any linear dependencies between data parameters;
• processing said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of free chlorine, said data values of any DBP to standardize the scales of said data values of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of free chlorine, said data values of any DBP;
• evaluating said data of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of free chlorine, said data values of any DBP 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 of pH, said data values of turbidity, said data values of conductivity, said data values of temperature, said data values of dissolved oxygen, said data values of total organic carbon, said data values of ammonia, said data values of free chlorine, said data values of any DBP are abnormal;
• and, in the event they all are not,
• estimating a valid likelihood that said water sample will be subject to TTHM 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.