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Development of hybrid algorithm of residual bootstrap artificial neural network based on sukuk volatility forecast model. by Nurul Hila Zainuddin

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Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

“…This reason motivated the researchers to exploit the evolution of machine learning to develop water level forecasting systems that were characterized by accuracy, simplicity and low cost. …”
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Box-jenkins and genetic algorithm hybrid model for electricity forecasting system by Mahpol, Khairil Asmani

“…The investigation is simulated using Intelligent Electricity Forecasting System (IEFS) developed in this research which written in Borland Delphi 7.0 programming.…”
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Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting by Afan H.A., Allawi M.F., El-Shafie A., Yaseen Z.M., Ahmed A.N., Malek M.A., Koting S.B., Salih S.Q., Mohtar W.H.M.W., Lai S.H., Sefelnasr A., Sherif M., El-Shafie A.

A systematic literature review of deep learning neural network for time series air quality forecasting by Zaini N., Ean L.W., Ahmed A.N., Malek M.A.

A big data prediction framework for weather forecast using MapReduce algorithm by Khalid, Adam, Mazlina, Abdul Majid, Fakherldin, Mohammed Adam Ibrahim, Jasni, Mohamad Zain

“…The process of weather forecasting is developing as the effect of advancement in technology right from the realization of increasing size of data, Weather forecasting was found to be based on big data. …”
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Long term energy demand forecasting based on hybrid, optimization: Comparative study by Musa, Wahab, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The objective of this research is to develop a long term energy demand forecasting model that used hybrid optimization.To accomplish this goal, a hybrid algorithm that combined a genetic algorithm and a local search algorithm method has been developed to overcome premature convergence.Model performances of hybrid algorithm were compared with former single algorithm model in estimating parameter values of an objective function to measure the goodness-of-fit between the observed data and simulated results.Averages error between two models was adopt to select the proper model for future projection of energy demand.…”
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Multi-horizon ternary time series forecasting by Htike@Muhammad Yusof, Zaw Zaw

“…This is mainly because state-of-the-art forecasting algorithms essentially perform single-horizon forecasts and produce continuous numbers as outputs. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

“…Artificial neural networks (ANN) are receiving a lot of attention of the researchers for these forecasts, because of their nonlinear mapping ability. …”
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Adaptive Fast Orthogonal Search (FOS) algorithm for forecasting streamflow by Osman A., Afan H.A., Allawi M.F., Jaafar O., Noureldin A., Hamzah F.M., Ahmed A.N., El-shafie A.

Balanced Stochastic Realization Algorithm For Development Of Rainfall Model by Azhari, Fahimy

“…In this research, Balanced Stochastic Realization (BSR) subspace algorithm is used to develop a rainfall model for Malaysia. …”
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A decision support system for improving forecast using genetic algorithm and tabu search by Ismail, Zuhaimy

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A Systematic Literature Review of Machine Learning Methods for Short-term Electricity Forecasting by Md Salleh N.S., Suliman A., Jorgensen B.N.

A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing by Wei , Yaxing

“…Given the multitude of components to manage, streamflow forecasting is preferable to employ an algorithm with low sensitivity to parameter variations. …”
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Investigating photovoltaic solar power output forecasting using machine learning algorithms by Essam Y., Ahmed A.N., Ramli R., Chau K.-W., Idris Ibrahim M.S., Sherif M., Sefelnasr A., El-Shafie A.

“…To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. …”

Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance by Allawi, Mohammed Falah, Jaafar, Othman, Mohamad Hamzah, Firdaus, Koting, Suhana, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…Obtaining successful operation rules for dam and reservoir systems is crucial for improving water management to meet the increase in agricultural, domestic and industrial activities. Several research efforts have been developed to generate optimal operation rules for dam and reservoir systems utilizing different optimization algorithms. …”
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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…In this research, a hybrid electricity price forecasting methodology is proposed using two-stage feature selection method and optimization using adaptive neuro-fuzzy inference system (ANFIS) technique as a forecasting engine. …”
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Optimization of operational policies for the Minab Reservoir, Southern Iran by Gholampoor, Mohammad

“…This research focuses on the optimization policies combined with a forecasting model for reservoir operation during drought conditions. …”
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Optimization of neural network architecture using genetic algorithm for load forecasting by Islam, B.U., Baharudin, Z., Raza, M.Q., Nallagownden, P.

“…The proposed technique provides a pathway to determine the best ANN architecture, prior to the training and learning process of neural network. Multi-objective algorithm is proposed in this research which optimizes the ANN architecture that leads to enhancement in load forecast accuracy and reduction in the computational cost. …”
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The forecasting of poverty using the ensemble learning classification methods by Zamzuri, Muhammad Haziq Adli, Nadilah, Sofian, Hassan, Raini

“…Random Forest and Extreme Gradient Boosting (XGBoost) algorithms were applied to forecast poverty since they are supervised learning algorithms that use the ensemble learning approach for classification. …”
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