Malaysian Meteorological Department Research Articles

Wind Profiles in Peninsular Malaysia: A Comprehensive Upper Air Analysis

Understanding the atmospheric properties and patterns is crucial in empowering Malaysia's national aerospace blueprint, national space, and legislation. While various policies have been enacted and implemented, there is a lack of information on the wind profile, specifically the upper air across Peninsular Malaysia. Realising the need to establish a standard guideline for national reference, future research, space-aerospace application, and legislation, this study was performed to develop the first wind profile analysis of upper air in Peninsular Malaysia. Relevant data from the Malaysian Meteorology Department was collected for analysis and evaluation. Specifically, a meteorology balloon attached with a sounding radiosonde was used to record data at 0000UTC (0800 h LT) and 1200UTC (2000 h LT) on the 15th day of each month for 7 years (from 2015 to 2021) at the KLIA and Kuantan Meteorology Stations for the wind profiling analysis. The daily overall data collection was recorded accurately once the balloon's rising rate stabilises from vertical air current (katabatic or anabatic winds). Subsequently, the collected data were evaluated in terms of the minimum, maximum, and average wind speeds for each year and time. Finally, the average wind speed of each year and time were combined to generate the Peninsular Malaysia wind profile. Based on the results, the projected wind profile for both stations identified three peaks of discrete sine wave flow type with low-speed wind profile in Peninsular Malaysia. The three peaks amplified the highest air velocity, whereby the 1st and 2nd peaks were located at the troposphere layer from 9,000 m to 12,000 m altitude (average wind speed of 10.8 ms-1) and 12,000 m to 18,000 m altitude (average wind speed of 13.7 ms-1). The 3rd peak was located at the stratosphere layer from 18,000 m to 32,000 m altitude (average wind speed of 15.2 ms-1). Since East Malaysia is located on the same equatorial line, the wind profile is hypothetically the same and exhibits only slight differences. In short, the established wind profile of upper air in Peninsular Malaysia in this study would facilitate other future studies and assist long-term planning of Malaysia's airspace legislation.

Associations between meteorological variation and hospitalisations for rotavirus infections in Kuala Lumpur, Malaysia

ObjectivesRotavirus (RoV) infections have emerged as a significant public health concern around the world. Understanding the relationship between climatic conditions and hospitalisations due to RoV infections can help engage effective prevention strategies. This study aims to investigate the potential associations between meteorological variability and RoV-related hospitalisations in Kuala Lumpur, Malaysia. MethodsHospitalization data from a tertiary teaching hospital in Kuala Lumpur over a twelve-year period were retrospectively collected. Concurrently, meteorological data were obtained from the Malaysian Meteorological Department (MetMalaysia) including variables of temperature, humidity, precipitation, and atmospheric pressure to further demonstrate relationship with RoV-associated hospitalisations. ResultsThe results indicated positive correlations between increased rainfall, rainy days, humidity, and RoV-related hospitalisations, suggesting the influence of environmental factors on the transmission of RoV. ConclusionsThis study highlights positive associations between meteorological variations and hospitalizations for RoV infections in Kuala Lumpur, Malaysia. Further investigations, including national-level data, are needed to deepen our understanding of these associations, particularly within the context of Malaysia and to develop targeted interventions for disease prevention and control.

Examining the roles of meteorological variables in COVID-19 spread in Malaysia

There are limited studies that established significant relationships between meteorological variables and COVID-19 spread in Malaysia. Although knowledge from studies conducted in other countries can be utilised as a basis for the policy formulation in general, country-specific studies are necessary as meteorological parameters and COVID-19 spread vary from country to country. This study investigated the potential roles of five meteorological parameters (average relative humidity, maximum temperature, average temperature, minimum temperature, and average wind speed) on COVID-19 spread in each of the 13 states and the entire Malaysia. The state-wise daily COVID-19-confirmed cases and daily meteorological parameters from 1 August 2020 to 31 March 2021 were acquired from Ministry of Health Malaysia and Malaysia Meteorological Department, respectively. The correlations between state-wise daily COVID-19-confirmed cases and daily meteorological parameters were ascertained via Pearson’s and Spearman’s correlation tests at 5% significance level. Overall, significant correlations exist between all meteorological parameters considered in this study and daily COVID-19-confirmed cases throughout Malaysia. Average wind speed was positively correlated with COVID-19-confirmed cases, whereas average relative humidity, maximum temperature, average temperature, and minimum temperature were negatively associated with COVID-19-confirmed cases. These study findings build up knowledge pertaining the association between important meteorological factors and COVID-19 spread in various regions with different climates. The research outcomes could be helpful in understanding, bringing awareness, and educating Malaysian citizens through communication between citizens and governmental as well as non-governmental agencies via official portals to develop a better response mechanism for the Malaysian perspective.

Usability Survey on Integrated Support System for Flood Disaster Management

Recovery and response in managing flood disaster requires effective communication system for information sharing and coordination between agencies. However, the flood rescue and relief operations in Malaysia is yet inefficient due to decentralised management system between humanitarian relief organisations. Hence, this study attempt to contribute through information technology solution by integrating roles of different agencies such as Ministry of Health (KKM), Malaysian Department of Social Welfare (JKM), Malaysian Meteorological Department (JMM) as well as nonprofit organisations (NGOs) in managing flood victim. Therefore, Waterfall methodology has been adopted in developing an online flood integrated support system for mitigating, preparing, responding, and recovering flood management process. The system acts as a centralised platform for flood rescue voluntary, disseminates flood-related information updates, and manage flood victim health status particularly in the Segamat district of Johore. An online usability survey by purposive sampling has been conducted with 30 public citizens representing prospectives system users. In addition, three individuals from flood management agencies are selected to represent the system administrator for evaluating the system usefulness through USE questionnaire. Findings for the usability survey suggested that the system usability is satisfactory. Likewise, while the admin features have been satisfactorily rated based on the USE questionnaires with some suggestions for future improvement. Extending the system features in managing registration and aid for the victims in the flood evacuation centre is recommended for future work.

An application of deep learning for lightning prediction in East Coast Malaysia

This paper presents the application of deep learning (DL) approach namely Feed-Forward Neural Networks (FFNN) in predicting the location of lightning occurrences within 100 km radius from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Pekan, Pahang Malaysia. The recorded data were obtained from Malaysia Meteorology Department (MET Malaysia), where the inputs of the DL are the intensity of the lightning in kilo Ampere, direction in degrees, distance and major axis that measures in km, while the output is the latitude and longitude of the lightning occurrences. The data are divided into training, validation and testing to measure the performance of the developed DL model. The findings of the study demonstrated the promising results of FFNN in terms of obtaining the minimum error which significantly increasing the accuracy of the predictions. To show the effectiveness of FFNN, the comparison study has been conducted with Long Short-Term Memory (LSTM) networks. From the simulation, it can be seen that FFNN can be used as an effective tool for predicting the location of lightning occurred better than the LSTM.

Investigation of the impacts of climate change and rising temperature on food poisoning cases in Malaysia.

This study is an attempt to investigate climate-induced increases in morbidity rates of food poisoning cases. Monthly food poisoning cases, average monthly meteorological data, and population data from 2004 to 2014 were obtained from the Malaysian Ministry of Health, Malaysian Meteorological Department, and Department of Statistics Malaysia, respectively. Poisson generalised linear models were developed to assess the association between climatic parameters and the number of reported food poisoning cases. The findings revealed that the food poisoning incidence in Malaysia during the 11 years study period was 561 cases per 100 000 population for the whole country. Among the cases, females and the ethnic Malays most frequently experienced food poisoning with incidence rates of 313 cases per 100,000 and 438 cases per 100,000 population over the period of 11 years, respectively. Most of the cases occurred within the active age of 13 to 35 years old. Temperature gave a significant impact on the incidence of food poisoning cases in Selangor (95% CI: 1.033-1.479; p = 0.020), Melaka (95% CI: 1.046-2.080; p = 0.027), Kelantan (95% CI: 1.129-1.958; p = 0.005), and Sabah (95% CI: 1.127-2.690; p = 0.012) while rainfall was a protective factor in Terengganu (95% CI: 0.996-0.999; p = 0.034) at lag 0 month. For a 1.0°C increase in temperature, the excess risk of food poisoning in each state can increase up to 74.1%, whereas for every 50 mm increase in rainfall, the risk of getting food poisoning decreased by almost 10%. The study concludes that climate does affect the distribution of food poisoning cases in Selangor, Melaka, Kelantan, Sabah, and Terengganu. Food poisoning cases in other states are not directly associated with temperature but related to monthly trends and seasonality.

Assessment Of Rainfall Pattern And Future Change For Kelantan River Basin, Malaysia Using Statistically Downscaled Local Climate Models

<p>Climate change has been discussed frequently in recent decades, and it has increased the probability of extreme flood occurrence. This study aims to provide an analysis of future rainfall patterns and flood occurrences specifically for the Kelantan River Basin which is identified as one of flood prone areas in Malaysia. The study area was divided into five regions of the Kelantan River Basin, - Kota Bharu (Northern), Kuala Krai (Center), Pos Lebir (Southeastern), Pos Hua (Southwestern) and Pos Gob (Northwestern). The historical rainfall data (1986-2019) was then retrieved from the Malaysian Meteorological Department (MMD) based on the five regions. The statistical approach was applied to downscaled climate model data from the CanESM2 GCM forced by the Representative Concentration Pathway (RCP) 4.5 and 8.5. The reliability assessment using a Cronbach’s Alpha, Linear Regression and Pearson Correlation results show that local climates (2006-2019) forced by RCP4.5 have a similar trend to historical rainfall within the same period. The spatial analysis outcomes showed that the northeastern region of the Kelantan River Basin received its highest average annual rainfall (5,000 mm) in 1990 and caused severe flooding in the area. However, there is a significant change of rainfall pattern in all regions, with a steady increase in annual rainfall in the southwestern region (2021-2100).</p>

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests’ Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures.

Modeling of Evaporation Rate for Peatland Fire Prevention Using Internet of Things (IoT) System

Peatland refers to the peat soil and wetland biological environment growing on the surface. However, unexpected fires in peatlands frequently have brought severe greenhouse gas emissions and transboundary haze to Southeast Asia. To alleviate this issue, this paper first establishes an Internet of Things (IoT) system for peatland monitoring and management in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, and proposes a more efficient and low-complexity model for calculating the Duff Moisture Code (DMC) in peatland forests using groundwater level (GWL) and relative humidity. The feasibility of the IoT system is verified by comparing its data with those published by Malaysian Meteorological Department (METMalaysia). The proposed Linear_DMC Model and Linear_Mixed_DMC Model are compared with the Canadian Fire Weather Index (FWI) model, and their performance is evaluated using IoT measurement data and actual values published by METMalaysia. The results show that the correlation between the measured data of the IoT system and the data from METMalaysia within the same duration is larger than 0.84, with a mean square error (MSE) of 2.56, and a correlation of 0.91 can be achieved between calculated DMC using the proposed model and actual values. This finding is of great significance for predicting peatland forest fires in the field and providing the basis for fire prevention and decision making to improve disaster prevention and reduction.

Seasonal variation in food security, lifestyle, nutritional status and its associated factors of the urban poor adolescents in Kuala Lumpur, Malaysia: research protocol of a prospective cohort study

BackgroundClimate change, obesity and undernutrition have now become a worldwide syndemic that threatens most people’s health and natural systems in the twenty-first century. Adolescent malnutrition appears to be a matter of concern in Malaysia, and this is particularly relevant among the urban poor population. Mounting evidence points to the fact that underlying factors of malnutrition are subject to climate variability and profoundly affect nutritional outcomes. Hence, it is interesting to examine seasonal variation in nutritional status and its associated factors of urban poor adolescents in Malaysia.MethodsThis is a prospective cohort study following urban poor adolescents aged 10–17 years living in low-cost high-rise flats in Kuala Lumpur, Malaysia, across two monsoon seasons. The baseline assessment will be conducted during the onset of the Northeast Monsoon and followed up during Southwest Monsoon. Climate data will be collected by obtaining the climatological data (rainfall, temperature, and relative humidity) from Malaysia Meteorological Department. Geospatial data for food accessibility and availability, and also built (recreational facilities) environments, will be analyzed using the QGIS 3.4 Madeira software. Information on socio-demographic data, food security, lifestyle (diet and physical activity), and neighbourhood environment (food and built environment) will be collected using a self-administrative questionnaire. Anthropometric measurements, including weight, height, and waist circumference, will be conducted following WHO standardized protocol. WHO Anthro Plus was used to determine the height-for-age (HAZ) and BMI-for-age (BAZ). Anaemic status through biochemical analyses will be taken using HemoCue 201+® haemoglobinometer.DiscussionThe study will provide insights into the seasonal effects in nutritional status and its associated factors of urban poor adolescents. These findings can be useful for relevant stakeholders, including policymakers and the government sector, in seizing context-specific strategies and policy opportunities that are seasonally sensitive, effective, and sustainable in addressing multiple challenges to combat all forms of malnutrition, especially among urban poor communities.Trial registrationThe protocol for this review has not been registered.

Prediction of energy consumption in campus buildings using long short-term memory

In this paper, Long Short-Term Memory (LSTM) was proposed to predict the energy consumption of an institutional building. A novel energy usage prediction method was demonstrated for daily day-ahead energy consumption by using forecasted weather data. It used weather forecasting data from a local meteorological organization, the Malaysian Meteorological Department (MET). The predictive model was trained by considering the dependencies between energy usage and weather data. The performance of the model was compared with Support Vector Regression (SVR) and Gaussian Process Regression (GPR). The experimental results with a dataset obtained from a building in Multimedia University, Malacca Campus from January 2018 to July 2021 outperformed the SVR and GPR. The proposed model achieved the best RMSE scores (561.692–592.319) when compared to SVR (3135.590–3472.765) and GPR (1243.307–1334.919). Through experimentation and research, the dropout method reduced overfitting significantly. Furthermore, feature analysis was done with SHapley Additive exPlanation to identify the most important weather variables. The results showed that temperature, wind speed, rainfall duration and the amount had a positive effect on the model. Thus, the proposed approach could aid in the implementation of energy policies because accurate predictions of energy consumption could serve as system fault detection and diagnosis for buildings.

"Analysis of Drought Index in Sub-Urban Area Using Standard Precipitation Evapotranspiration Index (SPEI)"

The climate change interference and increase of surface temperature have contributed to the changes at the atmosphere that give a significant effect to the availability of water. Malaysia is one of the countries which effected with this event and had been suffered series of drought events in 1997, 1998, and 2014. These problems contribute to the disruption of country’s economic development and quality of life. The using of drought indices for analytical is important and it can estimate the drought affect hat are being experienced accurately. The Standard Precipitation Evapotranspiration Index (SPEI) is one of the methods in determination of drought indices. Thus, the objective of this paper is to determine the drought index using the Standard Precipitation Evapotranspiration Index (SPEI) for sub-urban river basin which is Pahang River basin. The rainfall and potential evapotranspiration (PET) data were obtained from Malaysian Meteorological Department (Met Malaysia) starting from 1988 until 2018 in determining the SPEI value for sub-urban river basin. RStudio software are being used to determine the value of SPEI for different timescale, which is 3, and 12-month. The significant value of SPEI for the river basin are influence by the population, topography, rainfall, and land use. Findings from this study are believed to the mitigation can be proposed to the related organization to take an action in designing, planning, and managing the water supply and to prevent the serious impact on the agricultural and country economic development as well.

Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia

HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.

Nowcasting Earthquake Occurrence in Sabah

In line with the increasing availability of seismic data, nowcasting method provides a systematic way to depict the current state of earthquake hazard within a specific geographic area. This paper implements nowcasting method to statistically assess the earthquake progression in Sabah region over the large earthquakes cycle. The analysis includes earthquake data in the state of Sabah and the surrounding regions, which is acquired from Malaysia Meteorological Department catalogue and United States Geological Survey (USGS) catalogue from year 2009 to 2018. This study discovered that EPS corresponding to M ≥ 4 events in Ranau and Lahad Datu reach about 92% and 97%, respectively. These findings indicate that both areas are in the last quarter way through their cycle to magnitude 4.0 or larger earthquakes.

Wind speed prediction over Malaysia using various machine learning models: potential renewable energy source

Modeling wind speed has a significant impact on wind energy systems and has attracted attention from numerous researchers. The prediction of wind speed is considered a challenging task because of its natural nonlinear and random characteristics. Therefore, machine learning models have gained popularity in this field. In this paper, three machine learning approaches – Gaussian process regression (GPR), bagged regression trees (BTs) and support vector regression (SVR) – were applied for prediction of the weekly wind speed (maximum, mean, minimum) of the target station using other stations, which were specified as reference stations. Daily wind speed data, gathered via the Malaysian Meteorological Department at 14 measuring stations in Malaysia covering the period between 2000 and 2019, were used. The results showed that the average weekly wind speed had superior performance to the maximum and minimum wind speed prediction. In general, the GPR model could effectively predict the weekly wind speed of the target station using the measured data of other stations. Errors found in this model were within acceptable limits. The findings of this model were compared with the measured data, and only Kota Kinabalu station showed an unacceptable range of prediction. To investigate the prediction performance of the proposed model, two models were used as the comparison models: the BTs model and SVR model. Although the comparison of GPR with the BTs model at Kuching station showed slightly better performance for the BTs model in maximum and minimum wind speed prediction, the prediction outcomes of the other 13 stations showed better performance for the proposed GPR model. Moreover, the proposed model generated smaller prediction errors than the SVR model at all stations.

Innovative Trend Analysis of Reference Crop Evapotranspiration in Peninsular Malaysia

The inconsistencies in reference crop evapotranspiration (ET0) trends due to the occurrence of climate change have been detected over the world. This has substantially affected both the local and global water resources. The objective of this study is to investigate the historical trend of ET0 and its meteorological variables in Peninsular Malaysia. The meteorological data in daily scale, such as minimum, maximum and mean air temperature, relative humidity, wind speed and solar radiation covering the 2000-2019 period were obtained from Malaysian Meteorological Department and used to compute the ET0 estimation using FAO-56 Penman Monteith model. Then, innovative trend analysis was employed to detect the variations trends in ET0 and its meteorological variables. In the study area, the results showed that significant positive ET0 trends were found at Ipoh (8.09), Kuantan (15.10) and Subang (12.7620) stations respectively and no significant negative ET0 trend can be found. The finding of the study can be used to achieve support and improvement in the efficiency of irrigation regions and optimal water resources planning and utilization.

Statistical modelling of extreme temperature in Peninsular Malaysia

Extreme temperature events bring significant effects on the environment and society. Consequently, investigating the best fit for extreme temperature data is important for hydrological study and event forecasting. The main aim of this study is to determine the best fit probability distribution for monthly and annual extreme temperatures. The maximum temperature data at monthly and annual time scales were obtained from MMD (Malaysia Meteorological department). The temperature data for 40 years were fitted to the 10 probability distributions for each station. The parameters of the distributions were estimated by the maximum likelihood method and L-moment method. Besides, three goodness of fit tests, namely Kolmogorov-Smirnov (K-S), Anderson-Darling (A2) and Chi-Squared Error (CSE) test were applied to evaluate the performances of the distributions. The best fit distribution was selected based on the lowest test scores from the summation of the three goodness of fit tests. The results of this study showed that Generalized Extreme Value distribution was selected as the best-fit distribution, followed by Log-Pearson 3, 3 Parameter Lognormal, Generalized Log Logistic and Gamma distributions. The results of this study can be used as a reference for development planners, agricultural sector, water management agencies in hydrological planning and disaster management.

Development of Statistically Downscaled Regional Climate Model based on Representative Concentration Pathways for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia

Climate change is one of the challenging global issues that our world is facing and it is intensely debated on the international agenda. It is a fact that climate change has brought about many disastrous events on a global scale which affect our livelihoods. Climate models are commonly used by researchers to study the magnitude of the changing climate and to simulate future climate projections. Most climate models are developed based on various interactions among the Earth’s climate components such as the land surface, oceans, atmosphere and sea-ice. In this study, the second-generation Canadian Earth System Model (CanESM2) was statistically downscaled to develop a regional climate model (RCM) based on three representative concentration pathways (RCPs): RCP2.6, RCP4.5 and RCP8.5. The RCM will be used to simulate the average minimum and maximum temperatures and average precipitation for Ipoh, Subang and KLIA Sepang in Peninsular Malaysia for the years 2006 to 2100. The simulated data were bias corrected using the historical observation data of monthly average minimum and maximum temperatures and monthly average rainfall retrieved from the Malaysian Meteorological Department (MMD). The different trends of the simulated data for all the three locations based on the RCP2.6, RCP4.5 and RCP8.5 were evaluated for future climate projection.

Landings of Acetes spp. in the east coast of Sabah in the past 30 years (1990-2019)

The genus Acetes is a minor planktonic crustacean group, known by locals as ‘Udang Geragau’, ‘Udang Baring’ or ‘Bubok’ in different parts of Malaysia. The main objective of this research is to study the impact of climatic events on Acetes spp. landings at the East Coast of Sabah waters (Kudat and Tawau) in the past 30 years (1990-2019). Landing data on Acetes was obtained from the Fisheries Department of Sabah, Malaysia while the climatic data were provided by Malaysian Meteorological Department. One-way ANOVA analyses were conducted on the mean temperature, relative humidity, rainfall and the annual landings of Acetes spp. Pearson’s correlation test was later performed to measure relationship between the annual Acetes spp. landings and the variables as mentioned above. The results of correlation tests revealed that two variables (mean temperature and relative humidity) were having weak negative correlations with the annual landings in Kudat. Mean annual rainfall showed weak positive correlation with the Acetes landings in Tawau. The present study showed that changes in climatic event does influence the annual landings of Acetes spp. in the East Coast of Sabah.

Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.