Coastal Aquatic Conditions Reporting System Using A Learning Engine

The present invention relates to a software system that incorporates a digital learning engine comprised of machine learning algorithms that efficiently speeds up and expands the extraction of practically useful information from massively large data sets of observations and measurements of coastal aquatic environmental and human health conditions for the purpose of planning and implementing sustainable, preventative or mitigation actions by commercial, consumer, citizen, government, and research organizations.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to the field of monitoring, reporting, and researching the environmental conditions on or near the coasts, shore, or beaches of aquatic areas including freshwater, saltwater, and brackish water habitats.

Description of the Related Art

The capacity and capability of conventional manual and computer techniques for the processing and analysis of the threats to coastal aquatic conditions data for the purpose of planning and implementing sustainable, preventative, mitigation, or remediation actions by commercial, consumer, citizen, government, and research organizations is being exceeded by the size and increasing growth rate of the raw data being collected. Currently there is no platform or consolidated system that integrates wide varieties of data, that applies machine learning technology to automate and increase the productivity of the integration and analysis of the data, and that generates new action plans for improving the prevention, mitigation, and remediation of threats to coastal aquatic conditions habitats.

SUMMARY OF THE INVENTION

Water is fundamental to life and human activity. The global population is concentrated near bodies of fresh, salt, and brackish water. The United Nations estimates that 40% of the world's population lives within 100 km of ocean coastal areas and the vast majority of the remainder of the population lives with 100 km of other bodies of water such as rivers, streams, and estuaries. Defining coastal aquatic areas to include the coasts, shore, or beaches of bodies of fresh, salt, and brackish water, it is clear that the world economy and food supply chain is built on and around coastal aquatic areas.

As the global population and the global economy have grown and as environmental conditions have changed, there are growing threats to the quality of human life and health. The threat to the aquatic environment takes many forms such as pollution from chemical, plastics, and other debris, increases in temperature and acidity and decreases in oxygen content, increases in sea level, declining levels of aquatic animal and plant life, and increases in harmful blooms of algae. Many governments, non-governmental organizations, public and private organizations have launched growing efforts to measure, monitor, conduct mitigation experiments, and revise behaviors with the goals of understanding and addressing these threats. As more technology and public attention is applied with these two goals, the amount of data being collected is growing rapidly and becoming so massive that it creates significant opportunities and challenges. The opportunities include learning about how to make the global economy and population sustainable. The challenges include how to handle, analyze, and learn from the massive amounts of data being collected.

There are at least two basic challenges facing commercial, consumer, government, public, private, and research organizations about coastal aquatic conditions.

The first of the challenges if to create a deeper understanding of the relationships of natural and human factors that contribute to the causes, prevention, mitigation, and remediation of the threats to coastal aquatic conditions. There has been a significant increase into the use of a wide variety of technologies such as satellite-based sensors, drone platforms, surface and subsurface sensor platforms, and mobile devices in the hands of professional research, public interest, and government organizations as well as the public to collect and report data about the conditions on, in, or near bodies of water. This is producing a massive and rapidly increasing amount of data that needs to be analyzed and converted into useful information about the causes, prevention, mitigation, and remediation of the threats being discovered. While the growing amount of new data is expanding the archive of potentially useful data, there is a growing need for new and expanded methods for efficiently and effectively analyzing and learning from the data.

Multi-dimensional, multi-sourced, multi-media data is being collected by a wide variety of a growing number of sensors and sources (Ref. 1-26). The growing amount of data is outstripping the ability of conventional techniques to process it and convert it into useful, actionable information products. For example, data is being collected by acoustic sensors of the underwater sounds generated by weather, animal, and human, by fluidic and optical sensors of underwater microscopic manmade materials and plant and animal life, by human observational measurements of surface conditions, and by satellite systems of surface and weather conditions. The types of sensors and platforms collecting data include a wide range of stationary, active, passive, autonomous, manual, automated, and mobile platforms.

The second challenge is in what to do to prevent, mitigate, or remediate the threats to coastal aquatic areas. Because the economic, social, marine, and healthcare impact of each threat has become economically significant, there has been a growing number of companies, new and established, who are offering and marketing products and/or services designed to eliminate, reduce, or prevent the negative impact of such threats. This growth trend in new products and services is creating a growing amount of hypothesized, marketed, and speculated expectations as well a growing amount of experimental, testing, and operational performance data. There are few conventional methods, techniques, or organizations that are integrating, analyzing, and reporting information on how well and when new products and services work and what their cost effectiveness might be.

The list of users of this data is growing as well. The list ranges from government agencies who have responsibilities for reporting, mitigating, and remediating threats to coastal aquatic areas, to consumers whose livelihood or recreation are affected by these threats, to businesses that are affected by these threats, and to research organizations who study the causes, effects, and possible elimination of these threats.

While the amount of data being captured is growing rapidly and the demand for useful information is growing rapidly, the problem is that there are few technical solutions for converting the massive amounts of data into practically useful information about solutions, beneficial processes, and effective procedures.

The purpose of this invention is to unlock the information potential about the causes, effects, and relationships of the threats to coastal aquatic areas that may be available in the massively growing amounts of data being collected by a wide variety of people and organizations to serve the needs of researchers, government, consumers, and business.

Conventional techniques used by organizations that produce information about coastal aquatic conditions consist primarily of electronic platforms (web sites, mobile apps, radio and television reports, text blasts, email newsletters, etc.) that publish mostly raw data observations with manually inserted alert messages where appropriate. Reporting is extensive and broadly available but is disconnected, uneven in its quality, and often misinterpreted by the people and organizations that want to use it. Some reports cover weather conditions, some cover water conditions, some cover recreational conditions, some cover fishing conditions, some cover health conditions, etc. And the reports tend to be based on conditions as observed at the particular reporting period. Due to the massive amounts of data and the uneven quality or format of data, there are significant challenges in integrating data across time, geography, or altitude. There is a need for more effective or convenient methods or tools for combining all these sources of information and to learn from successes or failures of different combinations of parameters.

The present invention is an innovation and improvement over existing methods because it integrates data from many sources, speeds up analysis by orders of magnitude, and scales up the scope of learning from massive amounts of coastal aquatic conditions data in ways that have never been done before. The novelty of the invention is that it uses machine learning computational techniques and algorithms to process and analyze data sets from a variety of sensor and organizational sources and for a variety of phenomena. The product of such data set analysis by the machine learning algorithms is information in the form of what is called herein a set of policies. These policies comprise guidelines, best practices, mitigation and remediation products and procedures, and other forms of information about how a threat to coastal aquatic conditions can be legally and effectively addressed by people and organizations. The learning engine comprises a library of machine learning algorithms that include a combination of supervised and unsupervised learning methods that have been developed by academic and commercial organizations and are applied according to the nature, source, and quality of the raw data sets.

The net benefit of the use of the invention is to provide new discoveries to researchers and effective policies about the prediction, prevention, mitigation, and remediation of threats to coastal aquatic areas, new information that is valuable to and useful to businesses who make business decisions based on this information, to consumers who make recreational and buying decisions, government organizations that make enforcement, mitigation, and remediation decisions, and to researchers who make experimental program decisions.

DETAILED DESCRIPTION OF THE INVENTION

The embodiments described herein relate to a computer implemented method that includes a digital learning engine comprised of machine learning algorithms that economically scales up and speeds up the processing of massively large data sets of observations and measurements of coastal aquatic conditions to produce practically useful information in the form of what is called herein a set of policies. These policies comprise guidelines, best practices, mitigation and remediation products, procedures and processes, and other forms of information about how a threat to a coastal aquatic area can be legally and effectively addressed by people and organizations.

FIG. 1is Block Diagram of an embodiment of the layering of coastal aquatic condition data and other relevant data across time, space, and measurable or observable phenomena but which are collected, stored, managed, and analyzed in a wide variety of places by a wide variety of organizations. There is a massive amount of information in geographic information systems (GIS) created and managed by federal, state, and local government organizations that are organized in Geographic Data Base Layers110. These layers include Land Parcels111, Zoning112, Topography113, Wetlands114that include information about coastal aquatic areas, population and building density data in Demographics115, indications of Land Cover116such as natural, agriculture, landscaping, digital pictures from overhead cameras in Imagery117, and roads and natural features in Base Maps118. The Wetlands114layers in most GIS are usually limited to geographic features.

New data that has been growing massively in volume of collection and in breadth of phenomena in the category of Aquatic Conditions Data Layers120. The growing variety of technology-based Data Collection124platforms include active sensors, passive sensors, and human observations. There are a variety of Dynamic Models123that include hydrological, meteorological, and thermal computer models that are being developed and used in research organizations that are generating new data about the relationships of aquatic conditions inputs to outputs. There are expectations that Machine Learning122techniques that have been developed and applied to commercial applications such as retailing, cybersecurity, and games can be applied to the wide variety of coastal aquatic conditions data. The usefulness of the information from the analysis and learning from all of these data layers will be determined by the value and quality of Conditions Forecasts121for various natural and physical phenomena of coastal aquatic areas.

FIG. 2is a Block Diagram of an embodiment of Coastal Aquatic Conditions Reporting System Using a Learning Engine. The system receives and stores at least three digital libraries of data: Conditions Data210from sensors and human observations, Mitigation and Remediation Data220from government, commercial, and public sources, and Policy Data230from government, commercial, and public sources and from the Learning Engine240. The Learning Engine240processes new or historical data from the three digital libraries, learns from it, and creates new or improved polices that are used to update the Policy Data230library. The system also includes a User Interface250that provides information from the Learning Engine240or from the three digital libraries210,220, or230to either human or machine users.

FIG. 3is a Block Diagram of an embodiment of Data Sources for the Coastal Aquatic Conditions Reporting System Using a Learning Engine. There are several data sources that feed the digital library of the Conditions Data210. One group of sources includes Sensor Platforms311such as instrumented space craft, airborne vehicles, surface borne vehicles, underwater vehicles whether they are drones or human operated, and stationary platforms such as on buoys, piers, buildings, or towers. Another data source includes Citizens312who are people that record their observations of conditions in the form of digital images, voice recordings, or air or water quality with their own instruments or personal digital products such as cell phones. A third data source includes Government Organizations313at the federal, state, and local levels. A fourth data source includes Commercial Organizations314that maintain data bases or produce data products that describe the conditions or threats to coastal aquatic conditions in various geographic areas. A fifth data source includes Academic Organizations315that perform research, deliver educational courses, or maintain data bases or produce data products that describe the conditions of or threats to coastal aquatic conditions in various geographic areas. A sixth data source includes Non-Profit Organizations316that perform research, deliver educational courses, or maintain data bases or produce data products that describe the conditions of or mitigation approaches for threats to coastal aquatic conditions in various geographic areas.

There are several data sources that feed the digital library of the Mitigation Data220. One group of sources are Government Organizations321at the federal, state, and local levels. A second data source includes Commercial Organizations322that maintain data bases or produce data products that describe mitigation or remediation approaches for coastal aquatic conditions in various geographic areas. A third data source includes Academic Organizations323that perform research, deliver educational courses, or maintain data bases or produce data products that describe mitigation or remediation approaches for threats to coastal aquatic conditions in various geographic areas. A fourth data source includes Non-Profit Organizations324that perform research, deliver educational courses, or maintain data bases or produce data products that describe mitigation or remediation approaches for threats to coastal aquatic conditions in various geographic areas.

There are several data sources that feed the digital library of the Policy Data230. One group of sources are Government Organizations331at the federal, state, and local levels that perform research, deliver educational courses, or maintain data bases that describe products, practices, guidelines, principles, or legal constraints for mitigation or remediation approaches for threats to threats to coastal aquatic conditions in various geographic areas. A second data source includes Civic and Non-profit Organizations332that maintain data bases or produce products that describe practices, guidelines, principles, or legal constraints for using mitigation or remediation products for threats to coastal aquatic conditions in various geographic areas. A third data source includes Academic Organizations323that perform research, deliver educational courses, or maintain data bases or produce data products that describe products, practices, guidelines, principles, or legal constraints for mitigation or remediation approaches for threats to coastal aquatic conditions in various geographic areas. A fourth data source includes Trained Algorithms324that have been created or modified by the Learning Engine240that describe products, practices, guidelines, principles, or legal constraints for mitigation or remediation approaches for threats to threats to coastal aquatic conditions in various geographic areas.

A Block Diagram of an embodiment of a Learning Engine240for a Coastal Aquatic Conditions Reporting System is shown in the block diagram in shown inFIG. 4. At the heart of the Learning Engine240is a Master Knowledge Base450which is the digital archive of all layers of Conditions Data210, Mitigation Data220, and Policy Data230, the Learning Algorithms420where all the machine learning algorithms are stored and applied, and the Policy Generator Module430where all the policies for a Coastal Aquatic Conditions Reporting System are created and stored. The Learning Engine240includes a Data Cleaner440that corrects, converts, and reformats data received from Conditions Data210, Mitigation Data220, and Policy Data230. The Learning Engine240communicates with humans and machines through the User Interface250.

An embodiment of the Master Knowledge Base510for the Learning Engine240of the Coastal Aquatic Conditions Reporting System is shown in the block diagram inFIG. 5. The Master Knowledge Base510includes the storage of Conditions Data520, Policy Data530, and Mitigation Data540. Conditions Data520includes Beach Data521that includes waterfront, beach, and shore conditions, Water Quality Data522, Air Quality Data523, Boating Data524, Economic Data525, and Alerts Data526. Policy Data530includes Regulations Data531, Guidelines Data532, and Action Plan Data533. The Mitigation Data540includes Fixes Available541that describes mitigation or remediation solutions, products, and procedures that are available, Fixes Deployed542that describes mitigation or remediation solutions, products, and procedures that are being deployed by various organizations, and Forecasts543of coastal aquatic conditions.

An embodiment of the Data Cleaner440is shown in the Block Diagram ofFIG. 6. The function of the Data Cleaner440is to take raw data from the various sources and types of data such as Conditions Data Sources210, Mitigation Data Sources220, and Policy Data Sources230and Convert Into Master Knowledge Base Formats620. The function of cleaning data is necessary because data from the wide variety of sources often have problems that need to be identified, corrected, or annotated before they can be used by the Learning Algorithms or added to the Master Knowledge Base. Data problems occur because data formats for instruments and machines are not uniform, measurements made by machines as well as humans are contaminated in part with random noise, observational and technical biases, measurement data rates have different frequencies, amplitudes of measured values may not be absolute, and a variety of other problems. After the format conversion is completed, then the data cleaning process includes the steps of Identify and Replace Missing Data621, Identify and Correct Incorrect Data622, and Identify & Estimate Missing Data623.

An embodiment of the Learning Process420is shown in the Block Diagram ofFIG. 7. The Learning Process420comprises a library of machine Learning Algorithms710, data sets from Conditions Data210sources, Mitigation Data220sources, or Policy Data230sources all contained with the Master Knowledge Base510. During Training Calculations750one or more of the algorithms from the Learning Algorithms710digital library are used to determine whether the profiles in the Policy Data230need to be updated, modified, or new profiles created. The output of Training Calculations750is either new or updates to Policy Data230sets stored in the Master Knowledge Base510. As described above, each policy in Policy Data230is a profile of action steps contained within Regulations531, Guidelines532, or Action Plans533for mitigating coastal aquatic conditions for specific algae species and for specific geographic and water conditions. The machine learning algorithms that reside in the digital library Learning Algorithms710include Supervised720algorithms, Unsupervised740algorithms, and Semi-supervised730algorithms.

The Supervised720digital library of algorithms includes Regression721algorithms and Classification722algorithms. Supervised machine learning generally refers to the use of human experts to define the types of models or labels to be trained by data sets. In essence, a machine learning algorithm is supervised by a human expert as it calculates the best matches based on the data the algorithm is presented.

The algorithms in the Regression721digital library can be chosen from a variety of sources. Regression721algorithms are designed to calculate coefficients for a polynomial that produces a best fit between the polynomial equation and many sets of data. This best fit polynomial then becomes the new or updated model for a Plan which is a set of Rules for how to grow a specific species in a specific facility. The calculations and simulations used to determine the best fit model is the training process for the new or updated Plan or set of Rules.

The algorithms in the Classification722digital library can be chosen from a variety of sources. Classification722algorithms are designed to split data into categories which have labels that have been discovered or predefined by human experts. There are a variety of classification algorithms which use different types of equations to determine best fit within a classification.

The mathematical approaches that can be used in Supervised720algorithms for both Regression721and Classification722applications include Least Squares723, Bayesian724, Neural Nets725, Random Forests726, and Support Vectors727. Least Squares723algorithms compute the coefficients for a polynomial that makes the distance between data points and the polynomial as small as possible. In Least Squares723algorithms, there are no assumptions about what causes the differences between the data sets and the polynomial models. In Bayesian724algorithms, assumptions are included that the causes of the differences between the data sets and the polynomial models are statistical in nature. The typical assumptions in Bayesian724models include that the distribution is normal and that the mean and variance are known. In Neural Nets725algorithms, regression or classification polynomial calculations are organized as a parallel processing problem by assigning and modifying the weights or coefficients of the polynomial terms they flow through one or more hidden layers of parallel states. In Random Forest726algorithms, data sets are randomly selected, used to create several different decision trees often by different human experts, and then statistically merged or averaged together to produce a set of coefficients for matching polynomials or categories. In Support Vectors727machines, the approach to classifying sets of data is to calculate a polynomial model surface that separates the categories of data best rather than calculating a polynomial surface that fits the data within a category best. The coefficients of the polynomial that describes the separating plane can be represented as a vector in matrix algebra.

The Unsupervised740digital library of algorithms includes Clustering741algorithms and Association742algorithms. Unsupervised740algorithms are called unsupervised because an assumption is made that there is no set of labels or categories predefined by human experts that can be used to supervise, guide, or set the starting point for the machine learning calculations. Unsupervised machine learning algorithms are sometimes called data mining algorithms because the algorithms are mining or searching for some unknown classifications or labels from raw data.

Clustering741machine learning algorithms include the use of mathematical techniques for grouping a set of data in such a way that data in the same group (called a cluster) are more similar (in some calculable sense) to each other than to data in other groups (clusters). Because the clustering approach is unsupervised, it usually requires several iterations of analysis until consistently clear categorizations and groupings can be identified from the data sets being analyzed.

The Clustering741digital library includes the K-means743algorithm. The approach of K-means743algorithms is based on calculating the average distance between the centroid of K clusters in a dataset. At the start of the analysis, a number is chosen for K. Every data point is allocated to each of the K clusters through reducing the in-cluster sum of squares difference from each of the centroids. This process is iterative and takes several steps to correct each centroid location and minimize the sum of squares of the distances from the data points in each cluster to the centroid. Then a lower value of K and a higher value of K can be chosen to see if either of those numbers of clusters produces a lower mean or tighter fit. The iterations end when a value of K is found which produces the lowest sum of squares difference.

Association842machine learning algorithms include the use of correlation calculations to identify important relationships between categories or clusters of items in a data set. Relationships discovered by association machine learning algorithms can be used to generate new labels or categories for additional machine learning algorithm calculations. Apriori742is a digital library of algorithms that search for a series of frequent sets of relationship in datasets. For example, assume that a data set has five categories identified such as A, B, C, D, and E and that an association algorithm has identified a relationship between category A and B (e.g. if a data set has data in category A, 50% of the time it has data in a category B). An Apriori algorithm might find that if a data set has data in categories A and B, it has data in Category C 80% of the time.

Because it is not always possible to have data sets that can be analyzed with Supervised720algorithms and because it is sometimes expensive and difficult to use only Unsupervised740algorithms, an approach which speeds up the analysis process is to use a Semi-supervised730approach to using machine learning algorithms. The Semi-supervised730learning approach consists of a two-step process whereby a small amount of data is used to train in a Partial Supervised731approach which is then combined with a large amount of data used in a Partial Unsupervised732approach. Markov733algorithms and can then be applied to complete the training calculations of the Semi-supervised approach.

An embodiment of the Policy Generator430is shown in the Block Diagram inFIG. 8. The product of the Learning Process420is one or more policy profiles that have been trained by the machine Learning Algorithms710digital library using new or previously unused data the Conditions Data210and Mitigation Data220. When a newly trained policy profile is produced, a Policy Evaluation810is performed to determine if Create New Policy820is required or if Update Existing Policy830is required. In either case, the new or updated policy profile is added to the Policy Data230digital library in the Master Knowledge Base510.

An embodiment of the User Interface250is shown in the Block Diagram inFIG. 9. The User Interface250supports the delivery of Human Readable Information252and Machine-Readable Information254. Machine Readable Information254represents the data and information exchanged electronically between the instruments, equipment, and machines that are installed through an electronic network. The Human Interface Preparation910module manages the format and exchange of information between the Master Knowledge Base and Human Readable Information252through the Mobile Device911, Web Page912, and Desktop913. The Machine Interface Preparation920module converts the Machine-Readable Information254being communicated to the proper format for the Target Machine921.