Water resource management in the agricultural sector is a necessity that profoundly impacts water resources conservation for future generations. With the aim of rivers water abstraction (RWA) estimating for agricultur...
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Water resource management in the agricultural sector is a necessity that profoundly impacts water resources conservation for future generations. With the aim of rivers water abstraction (RWA) estimating for agricultural uses, the current research was implemented, in Hashtgerd and Zarand-Saveh sub-catchments of Iran;classified in Csa and Bsk-Csa climate categories with water scarcity situation. The RWA variables were estimated based on morphological, hydrological, and land-use characteristics and their combinations. Estimation of RWA was operated by applying single and integrated-wavelet (integrated-W with noise reduction) soft-computing methods, including Artificial Neural Networks (ANNs), Wavelet-ANN (WANNs), Adaptive Neural Fuzzy Inference System (ANFIS), Wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and Wavelet-GEP (WGEP). The WGEP model's efficiency with the hybrid characteristics of river width (RW), river depth (RD), minimum flow rate (Q(Min)), maximum flow rate (Q(Max)), average flow rate (Q(Mean)), cultivated area (CA), and orchard area (OA) variables, was recommended as the best model to estimate RWA variables without climate conditions' effects. The obtained values of RMSE for hybrid characteristic of WGEP models were 26.310 and 61.256 (*10(3) m(3)), for estimating RWA in Hashtgerd and Zarand-Saveh, respectively. The efficiencies of WGEP were excellent (R > 0.900) in the estimation of RWA in both climatic classes for maximum extreme values. Extracting mathematical formulation as part of the study's result profoundly impacts implementing policies related to integrated water resources management (IWRM). Also, the modeling of the variables affected the optimal rate of agricultural RWA for advance measures of balancing policies improvement in supply and demand of water resources in the agricultural sector, modification of cultivation patterns to save water consumption, providing food security to communities, and reforming the pattern of riverbeds
This study investigates the use of satellite imagery for robotics applications, specifically focusing on evaluating a hybrid satellite image segmentation method against conventional soft-computing techniques. The rese...
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ISBN:
(纸本)9798350373981;9798350373974
This study investigates the use of satellite imagery for robotics applications, specifically focusing on evaluating a hybrid satellite image segmentation method against conventional soft-computing techniques. The research is conducted in Ankara, where a satellite image is processed and segmented into road, building, vegetation/forest, and ground categories using several methods. Supervised methods are trained on sample images from the city district satellite image and compared to manual segmentation conducted in Adobe Photoshop. Results from the Feed-forward Neural Network (FNN) and Probabilistic Neural Network (PNN) are contrasted with the Hybrid method, which demonstrates robust performance in identifying features such as buildings, roads, ground, and forest. The hybrid method achieves a higher Kappa coefficient (0.4060) compared to FNN (0.3908) and PNN (0.3757), indicating superior segmentation accuracy. Challenges persist in distinguishing similar color spectrums, particularly between ground and other classes. Future research directions involve refining color-based feature extraction methods and exploring advanced machine learning techniques to enhance segmentation accuracy, especially in complex urban environments. The application of satellite imagery holds great potential for robotic mapping and navigation, with opportunities to integrate multi-modal data sources and deploy deep learning architectures for more reliable performance. As researchers continue to innovate in satellite image analysis, the prospects for transformative advancements in robotics applications remain compelling.
The fuzzy model derived from the input-output data of a given system using a fuzzy clustering algorithm is not generally optimal due to the errors introduced by the projection of the clusters onto the input variables ...
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ISBN:
(纸本)9781509040629
The fuzzy model derived from the input-output data of a given system using a fuzzy clustering algorithm is not generally optimal due to the errors introduced by the projection of the clusters onto the input variables and its approximation by the parametric functions. In the present work, the evolutionary algorithms are used to improve the initial fuzzy model obtained by fuzzy clustering algorithms. The proposed approach is applied to predict the form function of aluminum/polymer composite cylindrical shell. For this application, the performance of four fuzzy clustering algorithms: Gustafson Kessel (GK), fuzzy c-means (FCM), Gath-Geva algorithm (GG) and fuzzy c-regression model (FCRM) are optimized by genetic algorithm (GA) and hybrid particle swarm optimization with constriction factor approach (HPSO with CFA). The search space of evolutionary algorithms is restricted by the constraints in order to avoid loss of initial fuzzy model meaning. The achieved results show that the proposed approach is useful to improve the performance of an initial fuzzy model which is not optimal. The optimal model to predict the considered form function is FCM GA with a mean square error about 0.2497 and coefficient correlation of 0.9081.
Objective and motivation: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non...
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Objective and motivation: This work reports original results on the possibility of non-invasive temperature estimation (NITE) in a multilayered phantom by applying soft-computing methods. The existence of reliable non-invasive temperature estimator models would improve the security and efficacy of thermal therapies. These points would lead to a broader acceptance of this kind of therapies. Several approaches based on medical imaging technologies were proposed, magnetic resonance imaging (MRI) being appointed as the only one to achieve the acceptable temperature resolutions for hyperthermia purposes. However, MRI intrinsic characteristics (e. g., high instrumentation cost) lead us to use backscattered ultrasound (BSU). Among the different BSU features, temporal echo-shifts have received a major attention. These shifts are due to changes of speed-of-sound and expansion of the medium. Novelty aspects: The originality of this work involves two aspects: the estimator model itself is original (based on soft-computing methods) and the application to temperature estimation in a three-layer phantom is also not reported in literature. Materials and methods: In this work a three-layer (non-homogeneous) phantom was developed. The two external layers were composed of (in % of weight): 86.5% degassed water, 11% glycerin and 2.5% agar agar. The intermediate layer was obtained by adding graphite powder in the amount of 2% of the water weight to the above composition. The phantom was developed to have attenuation and speed-of-sound similar to in vivo muscle, according to the literature. BSU signals were collected and cumulative temporal echo-shifts computed. These shifts and the past temperature values were then considered as possible estimators inputs. A soft-computing methodology was applied to look for appropriate multilayered temperature estimators. The methodology involves radial-basis functions neural networks (RBFNN) with structure optimized by the multi-objective genetic alg
In this paper, a soft-computing based study aimed to estimate the available rotation capacity of cold-formed rectangular and square hollow section (RHS-SHS) steel beams is described and novel mathematical models based...
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In this paper, a soft-computing based study aimed to estimate the available rotation capacity of cold-formed rectangular and square hollow section (RHS-SHS) steel beams is described and novel mathematical models based on neural network (NN) and genetic expression programming (GEP) are proposed. In order to develop the proposed formulations, a wide experimental database obtained from available studies in the literature has been considered. The data used in the NN and GEP models are arranged in a format of eight input iiarameters covering both geometrical and mechanical properties such as width, depth and wall thickness of cross section, inside corner radius, yield stress, ratio of modulus of elasticity to hardening modulus, ratio of the strain under initial hardening to yield strain and shear length. The accuracy of the proposed formulations is verified against the experimental data and the rates of efficiency and performance are compared with those provided by analytical semi-empirical formulation developed by some of the Authors in a previous study. The proposed prediction models proved that the NN and GEP methods have strong potential for predicting available rotation capacity of cold-formed RHSSHS steel beams. (C) 2013 Elsevier Ltd. All rights reserved.
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