Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has\nbeen accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing\nday-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and\npaddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually\ncomplex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex,\nnonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple\nfood is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic\nparameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala,\nand Puttalam) with available data. Three training algorithms (Levenbergâ??Marquardt (LM), Bayesian Regularization (BR), and\nScaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each\nother to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the\nperformance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal\nthat LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic\nfactors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction\nprocess is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the\nprediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The\nANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data\nfrom various climate models....
This paper investigates the use of deep learning techniques in order to perform energy\ndemand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional\nneural network (CNN) coupled with an artificial neural network (ANN), with the main objective of\ntaking advantage of the virtues of both structures: the regression capabilities of the artificial neural\nnetwork and the feature extraction capacities of the convolutional neural network. The proposed\nstructure was trained and then used in a real setting to provide a French energy demand forecast using\nAction de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results\nshow that this approach outperforms the reference Réseau de Transport dâ??Electricité (RTE, French\ntransmission system operator) subscription-based service. Additionally, the proposed solution obtains\nthe highest performance score when compared with other alternatives, including Autoregressive\nIntegrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility\nof achieving high-accuracy forecasting using widely accessible deep learning techniques through\nopen-source machine learning platforms....
Technical advancements within the subject of artificial intelligence (AI) leads towards\ndevelopment of human-like machines, able to operate autonomously and mimic our cognitive\nbehavior. The progress and interest among managers, academics and the public has created a hype\namong many industries, and many firms are investing heavily to capitalize on the technology through\nbusiness model innovation. However, managers are left with little support from academia when\naiming to implement AI in their firmâ??s operations, which leads to an increased risk of project failure\nand unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used\nas a catalyst for business model innovation. Due to the increasing range and variety of the available\npublished material, a literature review has been performed to gather current knowledge within AI\nbusiness model innovation. The results are presented in a roadmap to guide the implementation\nof AI to firmâ??s operations. Our presented findings suggest four steps when implementing AI:\n(1) understand AI and organizational capabilities needed for digital transformation; (2) understand\ncurrent BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed\nto implement AI; and (4) reach organizational acceptance and develop internal competencies....
In multi-purpose reservoirs, to achieve optimal operation, sophisticated models are\nrequired to forecast reservoir inflow in both short- and long-horizon times with an acceptable\naccuracy, particularly for peak flows. In this study, an auto-regressive hybrid model is proposed for\nlong-horizon forecasting of daily reservoir inflow. The model is examined for a one-year horizon\nforecasting of high-oscillated daily flow time series. First, a Fourier-Series Filtered Autoregressive\nIntegrated Moving Average (FSF-ARIMA) model is applied to forecast linear behavior of daily flow\ntime series. Second, a Recurrent Artificial Neural Network (RANN) model is utilized to forecast\nFSF-ARIMA modelâ??s residuals. The hybrid model follows the detail of observed flow time variation\nand forecasted peak flow more accurately than previous models. The proposed model enhances\nthe ability to forecast reservoir inflow, especially in peak flows, compared to previous linear and\nnonlinear auto-regressive models. The hybrid model has a potential to decrease maximum and\naverage forecasting error by 81% and 80%, respectively. The results of this investigation are useful\nfor stakeholders and water resources managers to schedule optimum operation of multi-purpose\nreservoirs in controlling floods and generating hydropower....
The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an\nartificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA\n(PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all\nfour considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good\npozzolanic material.The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer\nand one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive\nmodel, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined\nusing the optimal ANN model closely followed the trend lines and surface plots of the experimental results....
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