Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions

Solar energy represents one of themost important renewable energy sources contributing to the energy transition process.Considering that the observation of daily global solar radiation(GSR)is not affordable in some parts of the globe,there is an imperative need to develop alternative ways to predict it.Therefore,the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions,such as the majority of Spanish territory.Here,four ensemble-based hybrid models were developed by hybridizing Additive Regression(AR)with Random Forest(RF),Locally Weighted Linear Regression(LWLR),Random Subspace(RS),and M5P.The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation.The testing phase outcomes demonstrated that the ARRF models outperform all other hybrid models.The provided models were validated by statisticalmetrics,such as the correlation coefficient(R)and root mean square error(RMSE).The results proved that Scenario#6,utilizing extraterrestrial solar radiation,relative humidity,wind speed,and mean,maximum,and minimum ambient air temperatures as the model inputs,leads to the most accurate predictions among all scenarios(R=0.968–0.988 and RMSE=1.274–1.403 MJ/m^(2)・d).Also,Scenario#3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations.The AD-RF model was the best predictive,followed by AD-RS and AD-LWLR.Hence,this study recommends new effective methods to predict GSR in semiarid regions.

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS environment.The information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were employed.The value of the Area Under the Curve(AUC)of ROC was above 0.80 for all models.For flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

Innovative configuration of vertical slot fishway to enhance fish swimming conditions

A vertical slot fishway(VSF)is among the most effective and commonly used fishway structures to enable fish to pass through artificial barriers such as dams and weirs in the river.Nevertheless,such structures need further improvements in providing better swimming conditions for fish inside the pool and enhancing attraction at the entrance.Therefore,the main objective of this study was to investigate the influence of slope and whether integrating some cylinder structures inside the fishway could enhance further attraction and provide better swimming conditions for fish.This study consists of several numerical simulations,first considering the fishway without cylinder elements while testing three different slopes under two different discharges.Then,the same numerical simulations were conducted,considering cylinder elements of different diameters and arrangements inside the fishway.The numerical model was validated by comparing computed velocities with those measured experimentally from the literature.The results show that the maximum velocity and turbulent kinetic energy(TKE)in the main jet increase as the discharge increases on the same slope.The flow velocity and TKE decrease in the areas inside the pool and between the two baffles.Introducing cylinder elements inside the fishway reduces the principal flow’s maximum velocity.Also,inside the pool,low-velocity regions were expanding.A comparison between the design with a cylinder and the simple VSF indicates that the presence of a cylinder reduced the maximum velocities for the smallest and highest slopes by 6.21%and 9.86%on average,respectively.However,in terms of TKE,cylinders inside the fishway could provide better performance than simple VSF,mainly for low-flow conditions.Finally,this study’s solution-oriented findings provide insights that could help design cost-effective fishways by improving particularly fish attraction to the fishway.