Computational fluid dynamics (CFD) provides numerical approximation to the equations that govern fluid motion. Application of the CFD to analyze a fluid problem requires the following steps. First, the mathematical equations describing the fluid flow are written. These are usually a set of partial differential equations. These equations are then discretized to produce a numerical analogue of the equations. The domain is then divided into small grids or elements. Finally, the initial conditions and the boundary conditions of the specific problem are used to solve these equations. All CFD codes contain three main elements: (1) A pre-processor, which is used to input the problem geometry, generate the grid, and define the flow parameter and the boundary conditions to the code. (2) A flow solver, which is used to solve the governing equations of the flow subject to the conditions provided. There are four different methods used as a flow solver: (i) finite difference method; (ii) finite element method, (iii) finite volume method. (3) A post-processor, which is used to massage the data and show the results in graphical and easy to read format.

Involvement of important engineering applications in the interaction of free-moving objects with dispersed multi-phase flows are numerous in recent years. However, due to the challenge and complexity, few studies have been reported and modeling approaches remain very limited. This paper reviews the use of discrete particle models (DPMs) for the study of the fluid flow phenomena and elaborates several different types of multiple phases. The efficiency of particle retention and computational time were considered in determining the most appropriate multiphase model. Therefore, the dynamic modeling of multi-scale systems requires a method to solve the problem of structural fluid interaction (FSI) in the particle fluid flow.

Current development in precision agriculture has underscored the role of machine learning in crop yield prediction. Machine learning algorithms are capable of learning linear and nonlinear patterns in complex agro-meteorological data. However, the application of machine learning methods for predictive analysis is lacking in the oil palm industry. This work evaluated a supervised machine learning approach to develop an explainable and reusable oil palm yield prediction workflow. The input data included 12 weather and three soil moisture parameters along with 420 months of actual yield records of the study site. Multisource data and conventional machine learning techniques were coupled with an automated model selection process. The performance of two top regression models, namely Extra Tree and AdaBoost was evaluated using six statistical evaluation metrics. The prediction was followed by data preprocessing and feature selection. Selected regression models were compared with Random Forest, Gradient Boosting, Decision Tree, and other non-tree algorithms to prove the R² driven performance superiority of tree-based ensemble models. In addition, the learning process of the models was examined using model-based feature importance, learning curve, validation curve, residual analysis, and prediction error. Results indicated that rainfall frequency, root-zone soil moisture, and temperature could make a significant impact on oil palm yield. Most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed, and root zone soil wetness. It is concluded that the means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data.

Rapid global economic development, population growth, and increased motorization have resulted in significant issues in urban traffic safety. This study explores the intrinsic connections between road environments and driving safety by integrating multiple visual landscape elements. High-resolution remote sensing and street-view images were used as primary data sources to obtain the visual landscape features of an urban expressway. Deep learning semantic segmentation was employed to calculate visual landscape features, and a trend surface fitting model of road landscape features and driver fatigue was established based on experimental data from 30 drivers who completed driving tasks in random order. There were significant spatial variations in the visual landscape of the expressway from the city center to the urban periphery. Heart rate values fluctuated within a range of 0.2% with every 10% change in driving speed and landscape complexity. Specifically, as landscape complexity changed between 5.28 and 8.30, the heart rate fluctuated between 91 and 96. This suggests that a higher degree of landscape richness effectively mitigates increases in driver fatigue and exerts a positive impact on traffic safety. This study provides a reference for quantitative assessment research that combines urban road landscape features and traffic safety using multiple data sources. It may guide the implementation of traffic safety measures during road planning and construction.

This review, “The Development of a River Quality Prediction Model That Is Based on the Water Quality Index using Machine Learning: A Review”, discusses and evaluates research articles and attempts to incorporate ML algorithms into the water quality index (WQI) to improve the prediction of river water quality. This original study confirms how new methodologies like LSTM, CNNs, and random forest perform better than previous methods, as they offer real-time predictions, operational cost saving, and opportunities for handling big data. This review finds that, in addition to good case studies and real-life applications, there is a need to expand in the following areas: impacts of climate change, ways of enhancing data representation, and concerns to do with ethics as well as data privacy. Furthermore, this review outlines issues, such as data scarcity, model explainability, and computational overhead in real-world ML applications, as well as strategies to preemptively address these issues in order to improve the versatility of data-driven models in various domains. Moving to the analysis of the review specifically to discuss the propositions, the identified key points focus on the use of complex approaches and interdisciplinarity and the involvement of stakeholders. Due to the added specificity and depth in a number of comparisons and specific technical and policy discussions, this sweeping review offers a broad view of how to proceed in enhancing the usefulness of the predictive technologies that will be central to environmental forecasting.

This paper presents a case study of dam where computational fluid dynamic (CFD) modelling is applied to simulate the three-dimensional (3D) flow hydraulics using computational aided program using ANSYS in order to analyse the existing fluid dynamic from reservoir to spillway of the dam due to long exposure to high pressure of water and high velocity of discharge. The purpose of this study is to ensure efficiency, reliability and safety of the dam generally and specifically on the spillway. Based on the findings, a model that have a worst condition based on both analysis is a model that have bigger area of the opening gate. This is because of the huge amount of the water release from the upstream to the downstream and produce a high value of water velocity and high impact from the water that fall from upstream towards the dam spillway. In order to avoid any sudden huge amount of the water release, the water level of the reservoir need to be check frequently. The water release from the dam need to be control to avoid any disaster to the environment and the dam structure.