Application of data-driven models to predict the dimensions of flow separation zone.

In this research, the effect of a submerged multiple-vane system on the dimensions of flow separation zone (DFSZ) is assessed via 192 measured datasets. The vanes' shape comprised two segments, curved and flat plates which are located in the connection of main channel to the lateral intake channel with an angle of 55°. In this direction, a butterfly's array for the vanes' arrangement along with different main controlling factors such as distances of vanes along the flow (δ), degree of curvature (β), and angles of attack to the local primary flow direction (θ) is utilized.

Assessment of groundwater vulnerability using remote sensing, susceptibility index, and WetSpass model in an arid region (Biskra, SE Algeria).

Biskra region currently shows signs of stress and a high risk of groundwater contamination by various chemicals and pesticides. For this purpose, a modified integrated susceptibility index (SI) is coupled with remote sensing (RS) and WetSpass model to assess the sensitivity of the groundwater and the risk of pollution in the most exploited aquifer (Quaternary aquifer) in the study area. The results of the modified SI model show that a major part of the aquifer is at risk of contamination if the farmers do not implement good agricultural practices.

Prognostication of scour around twin and three piers using efficient outlier robust extreme learning machine.

One of the most essential difficulties in the design and management of bridge piers is the estimation and modeling of scouring around the piers. The scour depth downstream of twin and three piers were simulated using a new outlier robust extreme learning machine (ORELM) model in this study. Furthermore, k-fold cross-validation with k = 4 was employed to validate the outcomes of numerical models.

Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan.

Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City.

Developing a boosted decision tree regression prediction model as a sustainable tool for compressive strength of environmentally friendly concrete.

One of the most significant parameters in concrete design is compressive strength. Time and money could be saved if the compressive strength of concrete is accurately measured. In this study, two machine learning models, namely, boosted decision tree regression (BDTR) and support vector machine (SVM), were developed to predict concrete compressive strength (CCS) using a complete dataset through the previous scientific studies.

Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models.

Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the operational period as well. In this research, soft computing models namely multi-layer perceptron neural network (MLPNN), support vector machine (SVM), multivariate adaptive regression splines (MARS), genetic programming (GP), M5 algorithm, and group method of data handling (GMDH) were used to predict the piezometric head in the core and the seepage discharge through the body of earth dam. For this purpose, the data recorded by the absolute instrument during the last 94 months of Shahid Kazemi Bukan Dam were used.

Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment.

One of the most critical parameters in concrete design is compressive strength. As the compressive strength of concrete is correctly measured, time and cost can be decreased. Concrete strength is relatively resilient to impacts on the environment.

Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia.

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.

Design of a hybrid ANN multi-objective whale algorithm for suspended sediment load prediction.

There is a need to develop an accurate and reliable model for predicting suspended sediment load (SSL) because of its complexity and difficulty in practice. This is due to the fact that sediment transportation is extremely nonlinear and is directed by numerous parameters such as rainfall, sediment supply, and strength of flow. Thus, this study examined two scenarios to investigate the effectiveness of the artificial neural network (ANN) models and determine the sensitivity of the predictive accuracy of the model to specific input parameters.