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Models, Theoretical

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Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

International journal of biometeorology Apr 27, 2024
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns th...
Forecasting Models, Theoretical Neural Networks, Computer Regression Analysis Machine Learning Gossypium Weather Rain
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Use of one-dimensional CNN for input data size reduction in LSTM for improved computational efficiency and accuracy in hourly rainfall-runoff modeling.

Journal of environmental management Apr 27, 2024
A deep learning architecture, denoted as CNNsLSTM, is proposed for hourly rainfall-runoff modeling in this study. The architecture involves a serial coupling of the one-dimensional convolutional neural network (1D-CNN) and the long short-term memory ...
Rain Hydrology Models, Theoretical Neural Networks, Computer Japan Deep Learning
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Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence.

Water research Apr 26, 2024
Groundwater models are essential for understanding aquifer systems behavior and effective water resources spatio-temporal distributions, yet they are often hindered by challenges related to model assumptions, parametrization, uncertainty, and computa...
Artificial Intelligence Models, Theoretical Groundwater Humans
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Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

Environmental science and pollution research international Apr 24, 2024
The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and ...
Geologic Sediments Neural Networks, Computer Models, Theoretical Deep Learning Environmental Monitoring Iran Rivers
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Prediction models for bioavailability of Cu and Zn during composting: Insights into machine learning.

Journal of hazardous materials Apr 23, 2024
Bioavailability assessment of heavy metals in compost products is crucial for evaluating associated environmental risks. However, existing experimental methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated exce...
Biological Availability Electric Conductivity Composting Hydrogen-Ion Concentration Phosphorus Copper Nitrogen Soil Models, Theoretical Zinc Neural Networks, Computer Soil Pollutants Machine Learning
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Transportation infrastructure upgrading and green development efficiency: Empirical analysis with double machine learning method.

Journal of environmental management Apr 23, 2024
In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and l...
Machine Learning Models, Theoretical Climate Change China Cities Sustainable Development Transportation
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Reconstructing transient pressures in pipe networks from local observations by using physics-informed neural networks.

Water research Apr 20, 2024
Reconstructing transient states presents significant challenges, particularly within complex pipe networks. These challenges arise due to nonlinear behaviours, inherent uncertainties in the system, and limitations in data availability. This work prop...
Water Supply Models, Theoretical Pressure Neural Networks, Computer
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Land subsidence prediction in coal mining using machine learning models and optimization techniques.

Environmental science and pollution research international Apr 19, 2024
Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertica...
Models, Theoretical Coal Machine Learning Coal Mining
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Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Water science and technology : a journal of the International Association on Water Pollution Research Apr 17, 2024
Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Comp...
Rivers Water Movements Regression Analysis Nonlinear Dynamics Machine Learning Canada Models, Theoretical
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PERform: assessing model performance with predictivity and explainability readiness formula.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis Apr 15, 2024
In the rapidly evolving field of artificial intelligence (AI), explainability has been traditionally assessed in a post-modeling process and is often subjective. In contrary, many quantitative metrics have been routinely used to assess a model's perf...
Models, Statistical Algorithms Humans Models, Theoretical Artificial Intelligence
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