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Daily Rainfall Forecasting Using Meteorology Data with Long Short-Term Memory (LSTM) Network by Soo See, Chai, Goh, Kok Luong

“…Rainfall is a natural climatic phenomenon and prediction of its value is crucial for weather forecasting. For time series data forecasting, the Long Short-Term Memory (LSTM) network is shown to be superior as compared to other machine learning algorithms. …”
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A big data prediction framework for weather forecast using MapReduce algorithm by Khalid, Adam, Mazlina, Abdul Majid, Fakherldin, Mohammed Adam Ibrahim, Jasni, Mohamad Zain

“…Weather forecasting plays a vital role in daily routine, businesses and their decisions. …”
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Short-term forecasting of daily confirmed COVID-19 cases in Malaysia using RF-SSA model by Shaharudin, Shazlyn Milleana, Ismail, Shuhaida, Hassan, Noor Artika, Tan, Mou Leong, Sulaiman, Nurul Ainina Filza

“…Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.…”
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Empirical analysis of parallel-NARX recurrent network for long-term chaotic financial forecasting by Abdulkadir, S.J., Yong, S.-P.

“…The experimental results based on mean absolute percentage error (MAPE) and other forecasting error metrics shows that P-NARX network trained with Bayesian regulation slightly outperforms Levenberg-marquardt, Resilient back-propagation and one-step-secant training algorithm in forecasting daily Kuala Lumpur Composite Indices. …”
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Daily rainfall prediction using clonal selection algorithm by Noor Rodi, Nur Syazwani, Ismail , Amelia Ritahani, Abdul Malik, Marlinda

“…Daily rainfall prediction is important in water resources management in order to estimate long term forecasting for future usage. …”
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Kelantan daily water level prediction model using hybrid deep-learning algorithm for flood forecasting by Loh, Eng Chuen

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Data normalization techniques in swarm-based forecasting models for energy commodity spot price by Yusof, Yuhanis, Mustaffa, Zuriani, Kamaruddin, Siti Sakira

“…Data mining is a fundamental technique in identifying patterns from large data sets.The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical.Prior to that, data are consolidated so that the resulting mining process may be more efficient.This study investigates the effect of different data normalization techniques.which are Min-max, Z-score and decimal scaling, on Swarm-based forecasting models.Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC).Forecasting models are later developed to predict the daily spot price of crude oil and gasoline.Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max.Nevertheless, the GWO is more superior than ABC as its model generates the highest accuracy for both crude oil and gasoline price.Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.…”
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A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting by Razak I.A.W.A., Abidin I.Z., Yap K.S., Abidin A.A.Z., Rahman T.K.A., Nasir M.N.M.

Air pollution forecasting in Kuala Terengganu using Artificial Neural Network (ANN) / Nur Raudzah Abdullah by Abdullah, Nur Raudzah

“…Existing researches on air pollution forecasting used a variety of machine learning algorithm. …”
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A new soft computing model for daily streamflow forecasting by Sammen S.S., Ehteram M., Abba S.I., Abdulkadir R.A., Ahmed A.N., El-Shafie A.

Development of a multi criteria decision support system using convolutional neural network and jaya algorithm for water resources management / Chong Kai Lun by Chong , Kai Lun

“…The proposed approach can be furthered categorized into two distinct stages: forecasting modeling and optimization modeling. Artificial neural network (ANN) has been widely used in forecasting tasks. …”
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Prediction of epidemic trends in COVID-19 with Mann-Kendall and recurrent forecasting-singular spectrum analysis by Shazlyn Milleana Shaharudin, Shuhaida Ismail, Mohd Saiful Samsudin, Azman Azid, Mou, Leong Tan, Muhamad Afdal Ahmad Basri

“…Nevertheless, enhanced RF-SSA algorithm should to be developed for higher effectivity in capturing any extreme data changes.…”
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Implementation of machine learning algorithms for streamflow prediction of Dokan dam by Sarmad Dashti Latif, Mr.

“…Daily inflow and rainfall time-series data have been collected as two hydrological parameters to forecast reservoir inflow using the developed deep learning long-short term memory (LSTM) model and conventional machine learning models, namely support vector machine (SVM), random forest (RF), and boosted regression tree (BRT). …”

WHALE OPTIMIZATION NEURAL NETWORK FOR DAILY WATER LEVEL FORECASTING CONSIDERING THE CHANGING CLIMATE by Kuok, King Kuok, Chiu, Po Chan, Md. Rezaur, Rahman, Teng, Yeow Haur

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Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia by Hanoon M.S., Ahmed A.N., Zaini N., Razzaq A., Kumar P., Sherif M., Sefelnasr A., El-Shafie A.

Improved prediction of daily pan evaporation using Bayesian Model Averaging and optimized Kernel Extreme Machine models in different climates by Ehteram M., Graf R., Ahmed A.N., El-Shafie A.

Application of augmented bat algorithm with artificial neural network in forecasting river inflow of hydroelectric reservoir stations in Malaysia by Joe Wee Wei, Mr.

“…Both standalone and hybrid models were developed to identify the most optimum parameter to be used for river SF forecasting. …”

Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia by Ridwan W.M., Sapitang M., Aziz A., Kushiar K.F., Ahmed A.N., El-Shafie A.

Mapreduce algorithm for weather dataset by Khalid Adam, Ismail Hammad

“…Weather forecasting plays a vital role in human daily routine, business and their decisions. …”
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A New Optimization Technique Of Support Vector Machine For Electricity Market Price Forecasting by Wan Abdul Razak, Intan Azmira, Zainal Abidin, Izham, Yap, Keem Siah, Sulaima, Mohamad Fani, Hassan, Elia Erwani, Gan, Chin Kim

“…Short term price forecasting such as day-ahead prediction provides forecast prices for a day-ahead (24 hours) up to few days ahead that is useful for daily operation. …”
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