"Our vision is to revolutionize environmental modeling by harnessing the power of machine learning to provide more accurate, efficient and scalable solutions for well-informed and defendable decision-making."
Here is how data science and ML is revolutionizing the environmental industry :
Air Quality Monitoring: Using machine learning algorithms to analyze data from air quality sensors to predict air pollution levels in real-time.
Environmental Impact Assessment (EIA): Using machine learning algorithms to analyze large amounts of data from EIA studies and quickly identify areas of potential environmental impact, improving the accuracy and efficiency of compliance reviews.
Water Quality Monitoring: Using machine learning algorithms to analyze water quality data and predict the potential for non-compliance with environmental regulations, allowing proactive measures to be taken. Random Forest (RF) for identifying the most important factors affecting groundwater quality.
Safety Monitoring: Implementing machine learning systems to monitor safety conditions in mines, predicting potential hazards and improving worker safety.
Reclamation Planning: Applying machine learning to analyze data on soil conditions, vegetation, and other factors to inform reclamation planning and minimize the long-term impact of mining on the environment.
Aquifer Characterization: Applying machine learning to analyze large amounts of geophysical data to better understand the structure and properties of aquifers and inform the sustainable management of groundwater resources. Support Vector Machines (SVMs) for classifying aquifer types and mapping groundwater potential zones.
Groundwater Recharge: Using machine learning to optimize the management of recharge activities, improve our understanding of the recharge process, and ensure sustainable use of groundwater resources. Decision Trees for groundwater recharge estimation and identifying recharge zones.
Predictive models for environmental contamination: Machine learning algorithms can be used to predict the presence and spread of toxic substances in the environment, such as in soil, water, or air. K-Nearest Neighbors (KNN) for mapping groundwater salinity and predicting water levels.
Chemical Fingerprinting: Creating a "fingerprint" of the mass spectral information, and using a neural network classifier to identify new samples based on their similarity to the fingerprinted mass spectra. The neural network classifier is trained using large datasets of mass spectrometric information and can accurately identify chemicals in complex mixtures, even in the presence of interferences or other confounding factors. This approach is used in contaminant source diffeentiation and provides a fast and reliable method for identifying source controbutions and distinguishing between closely related compounds.
Clustering algorithms for exposure assessment: Machine learning algorithms can be used to group individuals based on their exposure to toxic substances, providing valuable insights into patterns of exposure and risk. Gaussian Process Regression (GPR) for groundwater resource evaluation and estimation of transmissivity.