Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network

Abstract

Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production datadriven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R 2 ) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results. ARTICLE HISTORY

Flood Prediction Using Machine Learning Models: Literature Review

“…described how ML techniques could efficiently model complex hydrological systems such as floods. Many ML algorithms, e.g., artificial neural networks (ANNs) [44], neuro-fuzzy [45,46], support vector machine (SVM) [47], and support vector regression (SVR) [48,49], were reported as effective for both short-term and long-term flood forecast. In addition, it was shown that the performance of ML could be improved through hybridization with other ML methods, soft computing techniques, numerical simulations, and/or physical models.…”

“…Such applications provided more robust and efficient models that can effectively learn complex flood systems in an adaptive manner. Although the literature includes numerous evaluation performance analyses of individual ML models [49][50][51][52], there is no definite conclusion reported with regards to which models function better in certain applications.In fact, the literature includes only a limited number of surveys on specific ML methods in specific hydrology fields [53][54][55]. Consequently, there is a research gap for a comprehensive literature review in the general applications of ML in all flood resource variables from the perspective of ML modeling and data-driven prediction systems.Nonetheless, ML algorithms have important characteristics that need to be carefully taken into consideration.…”

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

“…Integrated approaches based on complex systems for forecasting the growth of sugarcane based on meteorological parameters using extreme machine learning and neural networks were able to show a more generalized model of forecasting for the growth of sugarcane, bringing benefits to industry and the community [52].…”

Evaluation of electrical efficiency of photovoltaic thermal solar collector

“…The market development of various solar thermal collectors was studied and compared with PV solar farms (Kramer & Helmers, 2013). To avoid time-consuming and also expensive experimental examinations in the PV/T systems, soft machine-based forecasting methods are developed (Chau, 2017;Chuntian & Chau, 2002;Fotovatikhah et al, 2018;Hajikhodaverdikhan, Nazari, Mohsenizadeh, Shamshirband, & Chau, 2018;Taherei Ghazvinei et al, 2018;Wu & Chau, 2011). These models can forecast the output efficiently based on some required input data.…”