Systems and methods for forecasting operational and strategic impacts of climate change on water quality and/or quantity

1 . A method comprising:

receiving, at a computer system from a plurality of sensors within a predefined geographic area, environmental data;

normalizing, via at least one processor of the computer system, the environmental data, resulting in normalized environmental data;

training, via the at least one processor using training data, a neural network;

pruning the neural network to remove nodes which are not useful in providing accurate outputs, resulting in a modified neural network;

executing, via the at least one processor, an artificial intelligence algorithm based on the modified neural network, wherein:

inputs to the artificial intelligence algorithm comprise:

the normalized environmental data; and

a list of categorized contaminants, wherein the list of categorized contaminants is generated by:

receiving, at the computer system via web scraping, a list of water contaminants;

stratifying, via the at least one processor, the list of water contaminants into categories based on commonalities, resulting in categories of contaminants; and

classifying, via the at least one processor, the list of water contaminants into the categories of contaminants, resulting in the list of categorized contaminants; and

outputs of the artificial intelligence algorithm comprise environmental risks, consequences, and probabilities associated with at least one environmental event; and

modifying at least one planned project using the environmental risks, consequences, and probabilities associated with the at least one environmental event.

2 . The method of claim 1 , wherein the environmental data comprises a list of water contaminants provided with a CAS (Chemical Abstracts Service) registration number of each chemical within the list of water contaminants.

3 . The method of claim 2 , the water contaminants comprising both regulated and unregulated contaminants.

4 . The method of claim 1 , wherein the artificial intelligence algorithm comprises a neural network.

5 . The method of claim 1 , further comprising:

generating, via the at least one processor, a water toxicity score based at least in part on the environmental risks, consequences, and probabilities associated with at least one environmental event.

6 . The method of claim 5 , further comprising:

transmitting, from the computer system to a terminal computer in response to a request from the terminal computer, the water toxicity score.

7 . 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, from a plurality of sensors within a predefined geographic area, environmental data;

normalizing the environmental data, resulting in normalized environmental data;

training, using training data, a neural network;

pruning the neural network to remove nodes which are not useful in providing accurate outputs, resulting in a modified neural network;

executing an artificial intelligence algorithm based on the modified neural network, wherein:

inputs to the artificial intelligence algorithm comprise;

the normalized environmental data; and

a list of categorized contaminants, wherein the list of categorized contaminants is generated by:

receiving, via web scraping executed by the at least one processor, a list of water contaminants;

stratifying, via the at least one processor, the list of water contaminants into categories based on commonalities, resulting in categories of contaminants; and

classifying, via the at least one processor, the list of water contaminants into the categories of contaminants, resulting in the list of categorized contaminants; and

outputs of the artificial intelligence algorithm comprise environmental risks, consequences, and probabilities associated with at least one environmental event; and

modifying at least one planned project using the environmental risks, consequences, and probabilities associated with the at least one environmental event.

8 . The system of claim 7 , wherein the environmental data comprises a list of water contaminants provided with a CAS (Chemical Abstracts Service) registration number of each chemical within the list of water contaminants.

9 . The system of claim 8 , the water contaminants comprising both regulated and unregulated contaminants.

10 . The system of claim 7 , wherein the artificial intelligence algorithm comprises a neural network.

11 . The system of claim 7 , 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:

generating a water toxicity score based at least in part on the environmental risks, consequences, and probabilities associated with at least one environmental event.

12 . The system of claim 11 , 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:

transmitting, to a terminal computer in response to a request from the terminal computer, the water toxicity score.

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

receiving, from a plurality of sensors within a predefined geographic area, environmental data;

normalizing the environmental data, resulting in normalized environmental data;

training, using training data, a neural network;

pruning the neural network to remove nodes which are not useful in providing accurate outputs, resulting in a modified neural network;

executing an artificial intelligence algorithm based on the modified neural network, wherein:

inputs to the artificial intelligence algorithm comprise:

the normalized environmental data; and

a list of categorized contaminants, wherein the list of categorized contaminants is generated by:

receiving, via web scraping executed by the at least one processor, a list of water contaminants;

stratifying, via the at least one processor, the list of water contaminants into categories based on commonalities, resulting in categories of contaminants; and

classifying, via the at least one processor, the list of water contaminants into the categories of contaminants, resulting in the list of categorized contaminants; and

outputs of the artificial intelligence algorithm comprise environmental risks, consequences, and probabilities associated with at least one environmental event; and

modifying at least one planned project using the environmental risks, consequences, and probabilities associated with the at least one environmental event.

14 . The non-transitory computer-readable storage medium of claim 13 , wherein the environmental data comprises a list of water contaminants provided with a CAS (Chemical Abstracts Service) registration number of each chemical within the list of water contaminants.

15 . The non-transitory computer-readable storage medium of claim 14 , the water contaminants comprising both regulated and unregulated contaminants.

16 . The non-transitory computer-readable storage medium of claim 13 , wherein the artificial intelligence algorithm comprises a neural network.

17 . The non-transitory computer-readable storage medium of claim 13 , having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:

generating a water toxicity score based at least in part on the environmental risks, consequences, and probabilities associated with at least one environmental event.