Most previous works on aboveground biomass (AGB) estimation provide a single estimate of AGB rather than the probability distribution of the predicted values. However, the NGBoost algorithm provides a probabilistic re...
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Most previous works on aboveground biomass (AGB) estimation provide a single estimate of AGB rather than the probability distribution of the predicted values. However, the NGBoost algorithm provides a probabilistic regression and uncertainty estimation solution. In this study, we validate NGBoost for estimating AGB in mangrove forests in northeastern Vietnam. We use spectral bands and image indices extracted from WorldView-2 as independent variables and field data from eight plots as the basis for analysis. By applying a spatial scaling sampling strategy, we derived approximately 290 aggregated samples from the established plots, which served as the dependent variables in subsequent modeling. To augment the training dataset and capture a broader spatial context, window filters of varying sizes were applied, enabling the inclusion of adjacent pixels into the sampling matrix. NGBoost hyperparameters were optimized by the meta-heuristic Fox-inspired optimization Algorithm using the Root Mean Square Error (RMSE) as the objective function. The trained model ended up at an RMSE of 1.8771, a Mean Absolute Error (MAE) of 1.2898, and an R2 of 0.924. We interpreted the trained model and found that the Green Leaf Index is the most influential factor in AGB estimation, far more than the following factors. Finally, we used the trained model to estimate AGB and its probability distribution for the entire study area.
Crowdsourcing has become increasingly popular in recent years as it enables requesters to find a group of workers to work on small tasks that an individual or organization cannot easily do. One of the main challenges ...
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The paper provides an analysis comparing three optimization algorithms, namely Elephant Herding optimization (EHO), Artificial Bee Colony (ABC) and Genetic Algorithm (GA). The study endeavors to scrutinize the efficac...
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With the continuous development of CBEC, logistics networks have become an important and essential component of the e-commerce market. However, in cross-border logistics networks, the complexity and uncertainty of the...
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Compared with multi-objective optimization problems(MOPs), constrained multi-objective optimization problems(CMOPs) have to consider the impact of constraints on seeking Pareto front in addition to convergence and div...
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Linearly constrained simulation optimization problems are those that include deterministic linear constraints in addition to an objective function that can only be evaluated through simulation. We provide several solv...
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Aiming at the problems of poor initial population diversity, slow convergence speed and easy to fall into local optimization of traditional WOA algorithm, a whale optimization algorithm improved by multi-strategy fusi...
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The creative products, generate a good experience, and ultimately realize the economic and cultural is a key issue that needs to be studied at present. Starting from fuzzy algorithms and aiming at the optimization in ...
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We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using_only local computations and communications. Previous...
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The Hetao Irrigation District (HID) is one of the three major irrigation districts in China, and the accurate estimation of the reference crop evapotranspiration (ETo) for effective water resource allocation and crop ...
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The Hetao Irrigation District (HID) is one of the three major irrigation districts in China, and the accurate estimation of the reference crop evapotranspiration (ETo) for effective water resource allocation and crop irrigation planning. In this study, the slime mould algorithm (SMA) was improved (named ISMA) by incorporating good point set initialization and reverse differential evolution methods. Daily meteorological data from five stations in the HID (2000-2014) were used to train and validate the ISMA model for ETo estimation. ISMA's optimization performance was benchmarked against SMA, particle swarm optimization (PSO), salp swarm algorithm (SSA), and honey badger algorithm (HBA) using 23 test functions, with results demonstrating ISMA's advantages in fast convergence, stability, and robustness. Six combinations of meteorological parameters (C1-C6) were evaluated, with the C6 combination (Tmax, Tmean, Tmin, RH, Rs, u2) achieving the best performance at all five stations, including lower MAE (0.085-0.098 mm d-1), MSE (0.015-0.019), RMSE (0.019-0.134 mm d-1), MAPE (4.14-5.11%), and the highest R2 (0.998). Additionally, the C4 combination (Tmax, Tmean, RH, Rs) also provided satisfactory estimation accuracy. The results highlighted the critical role of solar radiation as a key input for ETo modeling in HID. In conclusion, ISMA demonstrated high accuracy and adaptability in estimating daily ETo with limited meteorological data, offering valuable data support for water resource management and promoting the development of precision agriculture in the HID.
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