Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms

Peng, Wei; Sadaghiani, Omid Karimi

doi:10.1016/j.biombioe.2023.106884

Cited by 8 publications

(2 citation statements)

References 107 publications

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“…Forestry and agricultural practices yield enormous quantities of waste derived from farm yields [1]. The yearly production of biomass waste worldwide offers management issues, as discarded biomass could have adverse environmental effects [2]. Agricultural biomass residues or wastes are mainly fruit peels, crop stalks, roots, leaves, nutshells or seeds that are generally burned or discarded, but they are a potential supply of feedstock material [3].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

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Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.

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Section: Introductionmentioning

confidence: 99%

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Ragab,

Khadidos,

Alshareef

et al. 2023

Sustainability

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“…However, forecasting and monitoring forwarder productivity can provide forest managers with insights into demand forecasting, routing, behavioral analysis, facility planning, and, most importantly, truck traffic forecasting [26]. A gap in the literature was detected regarding the utilization of diverse machine learning techniques and empirical data pertaining to forest harvesting machines, planted forests, and working shifts in the estimation of forwarder productivity.…”

Section: Introductionmentioning

confidence: 99%

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Munis,

Almeida,

Camargo

et al. 2023

Forests

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Tactical planning in timber harvesting involves aspects related to forest macro-planning and, particularly, the allocation of resources and sequencing of activities, all of which affect the allocation of timber in forest yards and roads and the productivity of forest machines. Data-driven approaches encourage the use of information obtained from data to enhance decision-making efficiency and support the development of short-term strategies. Therefore, our investigation was intended to determine whether a data-driven approach can generate sufficient input for modeling forwarder productivity in timber forwarding in Pinus and Eucalyptus planted forests, to support tactical planning. We utilized 3812 instances of raw data that were generated over a 36-month period. The data were collected from 23 loggers who operated in Pinus and Eucalyptus planted forests. We applied 22 regression algorithms that applied a supervised learning method from an experimental machine learning approach to the data instances. We evaluated the fitted models using three performance metrics. Out of the tested algorithms, the default mode of light gradient boosting produced a root mean squared error of 14.80 m3 h−1, a mean absolute error of 2.70, and a coefficient of determination of 0.77. Therefore, data-driven methods adequately support forwarder productivity modeling in timber forwarding in planted forests and help forest managers with tactical planning.

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Water Research in the Age of AI: A Bibliometric Heuristic Analysis for Trends and Opportunities

Boutracheh,

Mejjad,

El Bouhadioui

et al. 2024

Lecture Notes in Geoinformation and Cartography

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Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms

Cited by 8 publications

References 107 publications

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Tactical Forwarder Planning: A Data-Driven Approach for Timber Forwarding

Water Research in the Age of AI: A Bibliometric Heuristic Analysis for Trends and Opportunities

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