Monitor 6: The model has not experienced dramatic or slow-leak regressions in training speed, All-in-one environmental monitoring equipment to collect real-time data on weather, noise & vibration to meet compliance requirements. Warning, Instrumentation and Monitoring, 3. 1.1. Meet environmental sustainability goals and accelerate conservation projects with IoT technologies. This project aims to make a case study using Machine Learning (ML) classification of sounds originating from the environment which are considered In WSN, the machine learning is considered as a tool that generates algorithms and patterns which are utilized to provide prediction models [].In particular, for environmental monitoring applications these predictive models can be proved essential as it can provide notifications of future occurring events by processing previously available data. The Future of Environmental Monitoring: Deep Learning and Artificial Intelligence. Monitoring the environmental impact is an important topic, and AI can help make this process more scalable, and automated. These devices will play a The rapid increase in both the quantity and complexity of data that are being generated daily in the field of environmental science and engineering (ESE) demands accompanied advancement in data analytics. Each of these use cases requires related but different ML models and system architecture, depending on their unique needs and environmental constraints. View Machine-Learning-Methods-for-Environmental-Monitoring-and-Flood-Protection.pdf from COMPUTER 001 at U.E.T Taxila. Let us propose a formal definition: Machine learning monitoring is a practice of tracking and analyzing production model performance to ensure acceptable quality as defined by the use case. General Context of Machine Learning in Agriculture.
research-article . Find the Rack PDU that fits your exact needs. Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. In the past decade, the Journal of chemical information and Limits of algorithms. Abstract and Figures. Discover a systematic approach to building, deploying, and monitoring machine learning solutions with MLOps. Machine learning and environmental science: an emerging field: In order to effectively solve the problem of traditional environmental monitoring system due to high sensor cost, difficult deployment, and high maintenance cost, the node Machine Learning Syllabus: Course Wise. However, the 326. Image by author. Audio Feature Extraction: short-term and segment-based. The most important task of the EWS is identification of the onset of critical situations affecting environment and population, early enough to inform the authorities and general public. In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. Microsoft 365 Microsoft Teams Windows 365 More All Microsoft Microsoft Security Azure Dynamics 365 Microsoft 365 Microsoft Teams Windows 365 Tech innovation This Special Issue aims to advance the application of machine learning algorithms for remote sensing-based environmental monitoring. Figure 1: Common machine learning use cases in telecom. 10 facts about jobs in the future AI and machine learning is currently being used to automate environmental inspections through AI analysis of images obtained by satellite or drone. Once configured, the machine learning engine begins analyzing observability data collected from Prometheus, Datadog or other observability tools to understand actual resource usage and application performance trends. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The four types of environmental monitoring are air quality, water quality, noise quality, and biodiversity. Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. We presented MAIA, a novel machine learning assisted method for image annotation in environmental monitoring and exploration. MAIA requires a reduced amount of manual interactions when compared to traditional annotation methods. We have used BIIGLE 2. These tools help in animation, unsupervised learning, avoid
This paper describes an online model based on sequential learning for real-time monitoring of dam displacement behavior. The present analysis is based on the estimation of the power spectral density (PSD) of a signal from its representation in the time-domain, see e.g. PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, We welcome methodological contributions in terms of novel machine learning strategies and innovative developments towards the reliability and robustness of the results. Image by author. This paper describes an approach for monitoring of flood protections systems based on machine learning methods.
Google Scholar Chen et al., 2012 Chen J. , Li Development of machine learning methods for improved fluorescence-based monitoring of environmental contaminants in surface waters Li, Ziyu Abstract. N the predictive analytics ai group, we build datadriven, highly distributed machine learning systemsOur engineers and researchers are responsible for architecting and Structured: Structured learning is suitable when we are aware of both inputs and outcomes. With the development of artificial intelligence and other associated models like machine learning, data science, industrial internet of things etc. It provides early warnings on performance issues and helps diagnose their root cause to debug and resolve.
Here, we predict the likelihood of a facility failing a water pollution inspection and propose alternative inspection allocations that would target high-risk facilities.
A robotic system for environment monitoring system based on Iot and data analytics using machine learning algorithm. Related Courses: Environmental monitoring systems are often Google Scholar Chen et al., 2012 Chen J. , Li M. , Wang W. , Statistical Uncertainty Estimation Using Random Forests and its Application to Drought Forecast , Mathematical Problems in Engineering , 2012 , 2012 . This Special Issue aims to advance the application of machine learning algorithms for remote sensing-based environmental monitoring. We welcome methodological contributions in terms of novel machine learning strategies and innovative developments towards the reliability and robustness of the results.
Machine learning for environmental monitoring M. Hino, E. Benami, N. Brooks Published 1 October 2018 Business Nature Sustainability Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives. October 1, 2018 Stanford students deploy machine learning to aid environmental monitoring Cash-strapped environmental regulators have a powerful and cheap new weapon. The implementation of machine learning methods for structural health monitoring applications has proven to be very powerful, especially in detecting damage and compensating Inspections are a critical part of keeping facilities of all kinds clean and running efficiently. Deep learning vs. machine learning vs. artificial intelligenceMachine learning is a subset of artificial intelligence that relies on computational models being able to iteratively learn patterns from input data and successively improve performance on specific data analysis tasks .It can include a number of techniques including deep learning, which relies on using data Self-regulated learning (SRL) is a critical 21st -century skill. Through machine learning, Torres is developing a program to scan the 25-year dataset in search of correlations for certain conditions.
From 2015 to 2020, the average concentration of PM 2.5 monitored by all 41 air quality monitoring stations in the study area was 52.95 g m 3, ranging from 2 to 494.9 g m Environmental Machine Learning is a program of fieldwork sessions with experiments as vehicles for materialising questions. Tinder brings people together. The researchers focused on the Clean Water Complex Environment, 6. In particular, malfunctions are compensated by learning virtual models of various particulate matter sensors. In addition, conventional indoor environmental monitoring, which is often considered a problem in only one scenario, lacks wide practical application potential. Considering environmental hazards endangering human health and applications of SPR in environmental monitoring, SPR has indicated great promise, especially in detecting environmental hazards with low molecular weights in complex matrices. Multiple machine learning and deep learning models are trained and evaluated on three landslide databases. Abstract. The main goal is to develop and Resilient Environmental Monitoring Utilizing a Machine Learning Approach. Real-time environmental monitoring systems are Unstructured: This type of learning is useful for complex problems where we dont know what the right answer is. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema World Academy of Science, Engineering and Technology 54 2011 Machine Learning Methods for Environmental Monitoring and Flood Protection Alexander L. Pyayt, Ilya I. Mokhov, Bernhard Lang, Valeria V. Krzhizhanovskaya, Robert J. Meijer infrastructure includes cloud and grid resources of the AbstractMore and more natural disasters are happening every UrbanFlood project, With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day and a presence in 190+
Environmental Machine Learning is a program of fieldwork sessions with experiments as vehicles for materialising questions. An [Journal of Korean Society for Atmospheric Environment]Evaluation and Prediction of Column Aerosol by Using the Time Series Machine Learning Technique LEMON 2022. AI + machine learning. Machine learning can automate, simplify and improve many aspects of water monitoring including: 1) Improving modeling and analysis 2) Detecting and correcting equipment malfunctions 3) Detecting environmental anomalies 4) Predicting the effects of policy decisions 5) Automating and controlling allocation and distribution The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. Environmental monitoring is the repeated measurement of physical, chemical and biological variables in order to study environmental changes, particularly those arising from human activities. The behavior monitoring model is the most widely used method in dam health monitoring, but existing methods still concentrate mainly on offline modeling or batch learning, neglecting the timeliness requirement. AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017 Carol Smith. Machine learning for classification in environmental monitoring In addition to prediction or disease state in the human system, coupling SML and microbial community profiling of microbial communities in the environment shows promise for the purpose of environmental monitoring [84] . Environmental monitoring controls pollution. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which
16KHz = 16000 samples per second).. We can now proceed to the next step: use these samples to analyze the
Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. Folio: 20 photos of leaves for each of 32 different species.
In this blog post we review common ML system components and their relationship to these different use cases. While previous literature used machine learning primarily to monitor prevailing needs in developing countries 20,21,22,23,24,25, our study uses machine learning to monitor Building on core material in 6.402, emphasizes the design and operation of sustainable systems. environmental applications.
For example, systems
The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. This research examined the This obstacle leads to a lack of regulation. Machine learning comes under Artificial Intelligence and BTech AI & ML, MTech AI & ML are some of the most popular courses for Machine Learning after 12th. They facilitate global trade flows with commodities and they form the basis of environmental monitoring technologies. Illustrates how to leverage heterogeneous data from urban services, cities, and the environment, and apply machine learning methods to evaluate and/or improve sustainability solutions.
As a quick recap, our engineers are always guided, first and foremost, by solving our customers real-world business problems. China has proposed two major measures to address the three rural issues: the first is to abolish the agricultural tax, which has been in place for over 2000 years; the second is A new field of Machine Learning called tinyML makes it possible to run a Machine Learning models on tiny, battery powered Internet of Things (IoT) devices. Monitor 5: The model is numerically stable. However, the Environmental Protection Agency cant inspect every facility each year. Machine learning methods can help optimize that process by predicting where funds can yield the most benefit. Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). [31, 32].The spectral analysis can be carried out by means of nonparametric and parametric methods: the latter ones are model-based and are able to account for a prior knowledge of the signal to get accurate spectral Machine learning can automate, simplify and improve many aspects of water monitoring including: 1) Improving modeling and analysis 2) Detecting and correcting equipment malfunctions 3) Detecting environmental anomalies 4) Predicting the effects of policy decisions In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). Chang and Bai, 2018 Chang N.B., Bai K., Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, CRC Press, 2018. This total includes some of Active Nodes, Idle Nodes, Unusable Nodes, Preempted They facilitate global trade flows with commodities and
Use machine learning to understand your images with industry-leading prediction accuracy. Monitor 4: Models are not too stale. Let us propose a formal definition: Machine learning monitoring is a practice of tracking and analyzing production model performance to ensure acceptable The isolation that is being provided using this service allows easier and faster data reporting and data analysis due The University of Minnesota announced today that it has received a three-year, $1.43 million grant from the National Science Foundation to advance machine learning
UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring Mark Parsons 1,*, Dmitry Bratanov 2 ID , Kevin J. Gaston 3,4 and Felipe Gonzalez 5 1 In this post, we Machine learning for predicting the surface plasmon resonance of perfect and concave gold nanocubes. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for Number of total nodes. Monitoring, logging, and application performance suite. This paper describes an approach for monitoring of flood protections systems based on machine learning methods. An Artificial Intelligence (AI) component has been developed for detection of abnormal dike behaviour. Also, the machine learning approach does not account for potential changes over time, such as in public policy priorities and pollution control technologies. AI technology has huge potential and can extend the reach and efficiency of environmental inspections and significantly enhance regulatory effectiveness. Public agencies aiming to enforce environmental regulation have limited resources Digital twin technology for water treatment GEICO is leading the way in application of Machine Learning and AI in the industry.
The increasing supply of earth monitoring (big) data, which is available through remote sensing, has also played a big role in increasing the potential for machine learning to be applied to complex, sometimes untapped, environmental problems. A variety of statistical and machine learning (ML) methods have been developed to discover hidden patterns and key factors in vast data sets and to improve groundwater monitoring or environmental contamination monitoring. PDU Product Selector. This environment has many beneficial effects for our system. Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earths population, climate changes [], natural resources depletion [], alteration of dietary choices [], as well as safety and health concerns [].As a means of addressing the
research-article . Find the Rack PDU that fits your exact needs. Intensive care unit (ICU) patients with venous thromboembolism (VTE) and/or cancer suffer from high mortality rates. In the past decade, the Journal of chemical information and Limits of algorithms. Abstract and Figures. Discover a systematic approach to building, deploying, and monitoring machine learning solutions with MLOps. Machine learning and environmental science: an emerging field: In order to effectively solve the problem of traditional environmental monitoring system due to high sensor cost, difficult deployment, and high maintenance cost, the node Machine Learning Syllabus: Course Wise. However, the 326. Image by author. Audio Feature Extraction: short-term and segment-based. The most important task of the EWS is identification of the onset of critical situations affecting environment and population, early enough to inform the authorities and general public. In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. Microsoft 365 Microsoft Teams Windows 365 More All Microsoft Microsoft Security Azure Dynamics 365 Microsoft 365 Microsoft Teams Windows 365 Tech innovation This Special Issue aims to advance the application of machine learning algorithms for remote sensing-based environmental monitoring. Figure 1: Common machine learning use cases in telecom. 10 facts about jobs in the future AI and machine learning is currently being used to automate environmental inspections through AI analysis of images obtained by satellite or drone. Once configured, the machine learning engine begins analyzing observability data collected from Prometheus, Datadog or other observability tools to understand actual resource usage and application performance trends. The process of machine learning for intelligent feature extraction consists of regular, deep, and fast learning algorithms. The four types of environmental monitoring are air quality, water quality, noise quality, and biodiversity. Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. We presented MAIA, a novel machine learning assisted method for image annotation in environmental monitoring and exploration. MAIA requires a reduced amount of manual interactions when compared to traditional annotation methods. We have used BIIGLE 2. These tools help in animation, unsupervised learning, avoid
This paper describes an online model based on sequential learning for real-time monitoring of dam displacement behavior. The present analysis is based on the estimation of the power spectral density (PSD) of a signal from its representation in the time-domain, see e.g. PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, We welcome methodological contributions in terms of novel machine learning strategies and innovative developments towards the reliability and robustness of the results. Image by author. This paper describes an approach for monitoring of flood protections systems based on machine learning methods.
Google Scholar Chen et al., 2012 Chen J. , Li Development of machine learning methods for improved fluorescence-based monitoring of environmental contaminants in surface waters Li, Ziyu Abstract. N the predictive analytics ai group, we build datadriven, highly distributed machine learning systemsOur engineers and researchers are responsible for architecting and Structured: Structured learning is suitable when we are aware of both inputs and outcomes. With the development of artificial intelligence and other associated models like machine learning, data science, industrial internet of things etc. It provides early warnings on performance issues and helps diagnose their root cause to debug and resolve.
Here, we predict the likelihood of a facility failing a water pollution inspection and propose alternative inspection allocations that would target high-risk facilities.
A robotic system for environment monitoring system based on Iot and data analytics using machine learning algorithm. Related Courses: Environmental monitoring systems are often Google Scholar Chen et al., 2012 Chen J. , Li M. , Wang W. , Statistical Uncertainty Estimation Using Random Forests and its Application to Drought Forecast , Mathematical Problems in Engineering , 2012 , 2012 . This Special Issue aims to advance the application of machine learning algorithms for remote sensing-based environmental monitoring. We welcome methodological contributions in terms of novel machine learning strategies and innovative developments towards the reliability and robustness of the results.
Machine learning for environmental monitoring M. Hino, E. Benami, N. Brooks Published 1 October 2018 Business Nature Sustainability Public agencies aiming to enforce environmental regulation have limited resources to achieve their objectives. October 1, 2018 Stanford students deploy machine learning to aid environmental monitoring Cash-strapped environmental regulators have a powerful and cheap new weapon. The implementation of machine learning methods for structural health monitoring applications has proven to be very powerful, especially in detecting damage and compensating Inspections are a critical part of keeping facilities of all kinds clean and running efficiently. Deep learning vs. machine learning vs. artificial intelligenceMachine learning is a subset of artificial intelligence that relies on computational models being able to iteratively learn patterns from input data and successively improve performance on specific data analysis tasks .It can include a number of techniques including deep learning, which relies on using data Self-regulated learning (SRL) is a critical 21st -century skill. Through machine learning, Torres is developing a program to scan the 25-year dataset in search of correlations for certain conditions.
From 2015 to 2020, the average concentration of PM 2.5 monitored by all 41 air quality monitoring stations in the study area was 52.95 g m 3, ranging from 2 to 494.9 g m Environmental Machine Learning is a program of fieldwork sessions with experiments as vehicles for materialising questions. Tinder brings people together. The researchers focused on the Clean Water Complex Environment, 6. In particular, malfunctions are compensated by learning virtual models of various particulate matter sensors. In addition, conventional indoor environmental monitoring, which is often considered a problem in only one scenario, lacks wide practical application potential. Considering environmental hazards endangering human health and applications of SPR in environmental monitoring, SPR has indicated great promise, especially in detecting environmental hazards with low molecular weights in complex matrices. Multiple machine learning and deep learning models are trained and evaluated on three landslide databases. Abstract. The main goal is to develop and Resilient Environmental Monitoring Utilizing a Machine Learning Approach. Real-time environmental monitoring systems are Unstructured: This type of learning is useful for complex problems where we dont know what the right answer is. In this paper, we examine SRL through the lens of the searching, monitoring, assessing, rehearsing, and translating (SMART) schema World Academy of Science, Engineering and Technology 54 2011 Machine Learning Methods for Environmental Monitoring and Flood Protection Alexander L. Pyayt, Ilya I. Mokhov, Bernhard Lang, Valeria V. Krzhizhanovskaya, Robert J. Meijer infrastructure includes cloud and grid resources of the AbstractMore and more natural disasters are happening every UrbanFlood project, With tens of millions of users, hundreds of millions of downloads, 2+ billion swipes per day, 20+ million matches per day and a presence in 190+
Environmental Machine Learning is a program of fieldwork sessions with experiments as vehicles for materialising questions. An [Journal of Korean Society for Atmospheric Environment]Evaluation and Prediction of Column Aerosol by Using the Time Series Machine Learning Technique LEMON 2022. AI + machine learning. Machine learning can automate, simplify and improve many aspects of water monitoring including: 1) Improving modeling and analysis 2) Detecting and correcting equipment malfunctions 3) Detecting environmental anomalies 4) Predicting the effects of policy decisions 5) Automating and controlling allocation and distribution The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. Environmental monitoring is the repeated measurement of physical, chemical and biological variables in order to study environmental changes, particularly those arising from human activities. The behavior monitoring model is the most widely used method in dam health monitoring, but existing methods still concentrate mainly on offline modeling or batch learning, neglecting the timeliness requirement. AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017 Carol Smith. Machine learning for classification in environmental monitoring In addition to prediction or disease state in the human system, coupling SML and microbial community profiling of microbial communities in the environment shows promise for the purpose of environmental monitoring [84] . Environmental monitoring controls pollution. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which
16KHz = 16000 samples per second).. We can now proceed to the next step: use these samples to analyze the
Mortality prediction in the ICU has been a major medical challenge for which several scoring systems exist but lack in specificity. Folio: 20 photos of leaves for each of 32 different species.
In this blog post we review common ML system components and their relationship to these different use cases. While previous literature used machine learning primarily to monitor prevailing needs in developing countries 20,21,22,23,24,25, our study uses machine learning to monitor Building on core material in 6.402, emphasizes the design and operation of sustainable systems. environmental applications.
For example, systems
The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. This research examined the This obstacle leads to a lack of regulation. Machine learning comes under Artificial Intelligence and BTech AI & ML, MTech AI & ML are some of the most popular courses for Machine Learning after 12th. They facilitate global trade flows with commodities and they form the basis of environmental monitoring technologies. Illustrates how to leverage heterogeneous data from urban services, cities, and the environment, and apply machine learning methods to evaluate and/or improve sustainability solutions.
As a quick recap, our engineers are always guided, first and foremost, by solving our customers real-world business problems. China has proposed two major measures to address the three rural issues: the first is to abolish the agricultural tax, which has been in place for over 2000 years; the second is A new field of Machine Learning called tinyML makes it possible to run a Machine Learning models on tiny, battery powered Internet of Things (IoT) devices. Monitor 5: The model is numerically stable. However, the Environmental Protection Agency cant inspect every facility each year. Machine learning methods can help optimize that process by predicting where funds can yield the most benefit. Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). [31, 32].The spectral analysis can be carried out by means of nonparametric and parametric methods: the latter ones are model-based and are able to account for a prior knowledge of the signal to get accurate spectral Machine learning can automate, simplify and improve many aspects of water monitoring including: 1) Improving modeling and analysis 2) Detecting and correcting equipment malfunctions 3) Detecting environmental anomalies 4) Predicting the effects of policy decisions In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). Chang and Bai, 2018 Chang N.B., Bai K., Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, CRC Press, 2018. This total includes some of Active Nodes, Idle Nodes, Unusable Nodes, Preempted They facilitate global trade flows with commodities and
Use machine learning to understand your images with industry-leading prediction accuracy. Monitor 4: Models are not too stale. Let us propose a formal definition: Machine learning monitoring is a practice of tracking and analyzing production model performance to ensure acceptable The isolation that is being provided using this service allows easier and faster data reporting and data analysis due The University of Minnesota announced today that it has received a three-year, $1.43 million grant from the National Science Foundation to advance machine learning
UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring Mark Parsons 1,*, Dmitry Bratanov 2 ID , Kevin J. Gaston 3,4 and Felipe Gonzalez 5 1 In this post, we Machine learning for predicting the surface plasmon resonance of perfect and concave gold nanocubes. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for Number of total nodes. Monitoring, logging, and application performance suite. This paper describes an approach for monitoring of flood protections systems based on machine learning methods. An Artificial Intelligence (AI) component has been developed for detection of abnormal dike behaviour. Also, the machine learning approach does not account for potential changes over time, such as in public policy priorities and pollution control technologies. AI technology has huge potential and can extend the reach and efficiency of environmental inspections and significantly enhance regulatory effectiveness. Public agencies aiming to enforce environmental regulation have limited resources Digital twin technology for water treatment GEICO is leading the way in application of Machine Learning and AI in the industry.
The increasing supply of earth monitoring (big) data, which is available through remote sensing, has also played a big role in increasing the potential for machine learning to be applied to complex, sometimes untapped, environmental problems. A variety of statistical and machine learning (ML) methods have been developed to discover hidden patterns and key factors in vast data sets and to improve groundwater monitoring or environmental contamination monitoring. PDU Product Selector. This environment has many beneficial effects for our system. Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earths population, climate changes [], natural resources depletion [], alteration of dietary choices [], as well as safety and health concerns [].As a means of addressing the