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What are the challenges of using machine learning to predict environmental outcomes?

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Machine learning (ML) is a branch of artificial intelligence that uses algorithms and data to learn from patterns and make predictions. ML has many applications in environmental engineering, such as monitoring air quality, forecasting water demand, optimizing waste management, and assessing climate change impacts. However, using ML to predict environmental outcomes also poses several challenges that need to be addressed by researchers and practitioners. In this article, we will discuss some of these challenges and how they can be overcome.

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Mohamad Yassine, Ph.D.

Serial Entrepreneur | Educator | Environmentalist

1 Data quality and availability

One of the main challenges of using ML to predict environmental outcomes is the quality and availability of data. Environmental data are often noisy, incomplete, inconsistent, or outdated, which can affect the accuracy and reliability of ML models. Moreover, environmental data are often scattered across different sources, formats, and scales, which can make it difficult to integrate and access them. To overcome these challenges, environmental engineers need to use appropriate data preprocessing techniques, such as cleaning, imputing, transforming, and aggregating data, to ensure that the ML models have sufficient and relevant data to learn from.

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Mohamad Yassine, Ph.D.

Serial Entrepreneur | Educator | Environmentalist
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As environmental data is inherently variable, complex, and noisy, ML applications in the field shall mitigate this by, for example, - Employing very large datasets if available - Reducing problem complexity by dimensionality reduction techniques. - Simulating processes stochastically and exploring noise theoretically

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Lidiane Lima

Oceanógrafa | Hidróloga | Cientista de dados | Pesquisadora | Consultora Ambiental | Modelagem ambiental | Redu??o de Risco de Desastres
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Os desafios de usar o aprendizado de máquina (ML) na predi??o de resultados ambientais é uma tarefa complexa, especialmente quando confrontados com a qualidade e disponibilidade de dados. A integra??o de dados de diversas fontes e formatos, além da atualiza??o contínua para refletir as mudan?as ambientais, é crucial para superar essas barreiras. Técnicas como limpeza de dados, imputa??o e transforma??o s?o essenciais para melhorar a qualidade dos dados antes da modelagem. Além disso, a colabora??o interdisciplinar pode facilitar o acesso a dados mais abrangentes e precisos, potencializando a eficácia dos modelos de ML na predi??o de fen?menos ambientais complexos.

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2 Model complexity and interpretability

Another challenge of using ML to predict environmental outcomes is the complexity and interpretability of the models. ML models can have many parameters, layers, or features, which can make them difficult to understand and explain. This can pose problems for environmental engineers who need to communicate their results to stakeholders, regulators, or policy makers, who may require transparency and accountability for the ML models. Moreover, complex ML models can also be prone to overfitting or underfitting, which can compromise their generalization and robustness. To overcome these challenges, environmental engineers need to use appropriate model selection, validation, and evaluation techniques, such as cross-validation, regularization, or sensitivity analysis, to ensure that the ML models are fit for purpose and can be justified.

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3 Uncertainty and variability

A third challenge of using ML to predict environmental outcomes is the uncertainty and variability of the data and the models. Environmental data are often affected by natural or human-induced variability, such as seasonal, spatial, or temporal changes, which can influence the ML models' performance and predictions. Moreover, ML models are often based on assumptions, simplifications, or approximations, which can introduce uncertainty and error in the predictions. To overcome these challenges, environmental engineers need to use appropriate uncertainty quantification and propagation techniques, such as confidence intervals, error bars, or Monte Carlo simulations, to ensure that the ML models' predictions are reliable and robust.

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4 Ethical and social implications

A final challenge of using ML to predict environmental outcomes is the ethical and social implications of the models. ML models can have significant impacts on environmental decision making, policy making, and management, which can affect the well-being of humans and ecosystems. However, ML models can also have potential biases, limitations, or risks, such as data privacy, algorithmic fairness, or environmental justice, which can affect the trustworthiness and acceptability of the models. To overcome these challenges, environmental engineers need to use appropriate ethical and social assessment techniques, such as stakeholder engagement, impact analysis, or value alignment, to ensure that the ML models are aligned with the environmental values and goals of the society.

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Lidiane Lima

Oceanógrafa | Hidróloga | Cientista de dados | Pesquisadora | Consultora Ambiental | Modelagem ambiental | Redu??o de Risco de Desastres
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O avan?o do aprendizado de máquina (ML) na predi??o de resultados ambientais oferece uma promessa inovadora para a conserva??o e gest?o de recursos naturais. A utiliza??o de ML na formula??o de políticas e decis?es ambientais carrega o peso da responsabilidade, pois o impacto dessas tecnologias transcende os dados, influenciando a vida de comunidades e ecossistemas inteiros. é imperativo que, ao empregar modelos de ML, engajemos em uma reflex?o profunda sobre sua justi?a algorítmica, privacidade de dados e potenciais vieses. Isso requer um esfor?o consciente para incorporar técnicas de avalia??o ética e social, garantindo que nossas inova??es estejam alinhadas com os princípios de justi?a ambiental e os valores da sociedade.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the challenges of using machine learning to predict environmental outcomes?

1

2

3

4

5

1 Data quality and availability

2 Model complexity and interpretability

3 Uncertainty and variability

4 Ethical and social implications

5 Here’s what else to consider

Environmental Engineering

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What are the challenges of using machine learning to predict environmental outcomes?

1

2

3

4

5

1 Data quality and availability

2 Model complexity and interpretability

3 Uncertainty and variability

4 Ethical and social implications

5 Here’s what else to consider

Environmental Engineering

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