Artificial intelligence and machine learning in water resources engineering

Danish, Mohd.

doi:10.1016/b978-0-323-91910-4.00001-7

Cited by 2 publications

(2 citation statements)

References 49 publications

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“…In the field of material science, ML contributes to the development of models for image classification using deep learning and Convolutional Neural Networks (CNNs), which are crucial for understanding and manipulating materials at a fundamental level [Samine, S etal 2022]. Similarly, in water resources engineering, ML algorithms facilitate smart planning and execution of projects, including flood prediction and mitigation, while also facing challenges such as data quality and computational costs [Danish M. 2022]. The chemical engineering process benefits from ML through datadriven solutions to imaging problems, aligning with industrial automation advancements like Industry…”

Section: B Importance Of Machine Learning In Engineeringmentioning

confidence: 99%

A Review of Machine Learning

Anusha

2024

IJSREM

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The ever-growing complexity of engineering systems and the vast amount of data generated necessitate advanced data analysis techniques like machine learning (ML). This review explores the motivations and advantages of integrating ML into real-world engineering applications. The paper highlights the limitations of traditional deterministic models in handling intricate interactions within modern engineering systems. ML offers significant benefits including improved efficiency, enhanced decision-making, advanced automation and control, adaptability, and cost savings. Real-world applications across various disciplines are explored, including image recognition and computer vision in self-driving cars, predictive maintenance for optimizing equipment lifespans, structural health monitoring for identifying potential damage, and signal processing for control systems in airplanes and traffic lights. The review concludes by summarizing key findings from case studies on autonomous vehicle navigation, smart grid optimization, wind turbine fault detection, and HVAC system optimization in smart buildings. These case studies showcase the power of machine learning techniques like deep learning, reinforcement learning, sensor fusion, and ensemble learning in achieving superior performance and efficiency in various engineering domains. Key Words: Machine Learning, Engineering Applications, Artificial Intelligence, Big Data, Predictive Maintenance, Robotics, Structural Design, Smart Grids, Fault Detection, Optimization, Control Systems.

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Section: B Importance Of Machine Learning In Engineeringmentioning

confidence: 99%

A Review of Machine Learning

Anusha

2024

IJSREM

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“…Flooding is a significant natural disaster that can cause severe damage to life and property. SVM and DT can analyse various data sources, such as weather data and historical flooding data, to predict the likelihood of flooding occurring in a specific region (Danish, 2022). Similarly, SVM and DT can analyse various data sources, such as historical earthquake data, tectonic plate movements, and seismic activity, to predict the likelihood of an earthquake occurring in a specific region (Teodoro and Duarte, 2022).…”

Section: Figurementioning

confidence: 99%

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

Jain,

Dhupper,

Shrivastava

et al. 2023

Front. Environ. Sci.

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Globally, communities and governments face growing challenges from an increase in natural disasters and worsening weather extremes. Precision in disaster preparation is crucial in responding to these issues. The revolutionary influence that machine learning algorithms have in strengthening catastrophe preparation and response systems is thoroughly explored in this paper. Beyond a basic summary, the findings of our study are striking and demonstrate the sophisticated powers of machine learning in forecasting a variety of weather patterns and anticipating a range of natural catastrophes, including heat waves, droughts, floods, hurricanes, and more. We get practical insights into the complexities of machine learning applications, which support the enhanced effectiveness of predictive models in disaster preparedness. The paper not only explains the theoretical foundations but also presents practical proof of the significant benefits that machine learning algorithms provide. As a result, our results open the door for governments, businesses, and people to make wise decisions. These accurate predictions of natural catastrophes and emerging weather patterns may be used to implement pre-emptive actions, eventually saving lives and reducing the severity of the damage.

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Artificial intelligence and machine learning in water resources engineering

Cited by 2 publications

References 49 publications

A Review of Machine Learning

A Review of Machine Learning

Leveraging machine learning algorithms for improved disaster preparedness and response through accurate weather pattern and natural disaster prediction

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