Browsing by Author "Rathnayake, U. S"

Now showing 1 - 20 of 55

Open Access
Analyzing relationships between rainfall and paddy harvest using artificial neural network (ANN) approach: case studies from North-western and North-central provinces, Sri Lanka
(The Faculty of Agricultural Sciences of the Sabaragamuwa University of Sri Lanka, 2022-01-04) Ranasinghe, T; Rathnayake, U. S; Gunawardena, G; Wimalasiri, E. M
Purpose: Food and agriculture are frequently affected from on-going climate change. A significant percentage of annual harvest is lost due to extreme climatic conditions in different parts of the world. Sri Lanka is considered as a country which is vulnerable to climate change. Therefore, this research presents a detailed analysis to find out the non-linear relationships between the rainfall and paddy harvest in two major provinces of Sri Lanka. Research Method: North-central and North-western provinces as two major agricultural areas were selected for the study. Rainfall trends were identified using non-parametric Mann-Kendall and Sen’s slope estimator tests. The artificial neural network (ANN) approach was used to establish non-linear relationships between rainfall and paddy yield. Findings: There was no significant (p > 0.05) linear correlation between rainfall amount and the rainfed paddy yield in tested locations. However, no clear relationship between the rainfall and rain fed yield were found in the 14 predefined functions (polynomial, logarithmic, exponential and trigonometric) derived using ANN where the calculated coefficients of determination were less than 0.3. Research Limitations: Due to lack of other climate variables such as temperatures, a significant relationship was not observed in this study. Originality/value: We have shown that non-linear artificial neural network approach can be used to study the impact of climate on agricultural production in Sri Lanka.
Open Access
Artificial Neural Network based PERSIANN data sets in evaluation of hydrologic utility of precipitation estimations in a tropical watershed of Sri Lanka
(AIMS Geosciences, 2021-09) Gunathilake, M; Senarath, T; Rathnayake, U. S
The developments of satellite technologies and remote sensing (RS) have provided a way forward with potential for tremendous progress in estimating precipitation in many regions of the world. These products are especially useful in developing countries and regions, where ground-based rain gauge (RG) networks are either sparse or do not exist. In the present study the hydrologic utility of three satellite-based precipitation products (SbPPs) namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Cloud Classification System (PERSIANN-CCS) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain Rate near real-time (PDIR-NOW) were examined by using them to drive the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrologic model for the Seethawaka watershed, a sub-basin of the Kelani River Basin of Sri Lanka. The hydrologic utility of SbPPs was examined by comparing the outputs of this modelling exercise against observed discharge records at the Deraniyagala streamflow gauging station during two extreme rainfall events from 2016 and 2017. The observed discharges were simulated considerably better by the model when RG data was used to drive it than when these SbPPs. The results demonstrated that PERSIANN family of precipitation products are not capable of producing peak discharges and timing of peaks essential for near-real time flood-forecasting applications in the Seethawaka watershed. The difference in performance is quantified using the Nash-Sutcliffe Efficiency, which was >0.80 for the model when driven by RGs, and <0.08 when driven by the SbPPs. Amongst the SbPPs, PERSIANN performed best. The outcomes of this study will provide useful insights and recommendations for future research expected to be carried out in the Seethawaka watershed using SbPPs. The results of this 479 AIMS Geosciences Volume 7, Issue 3, 478–489. study calls for the refinement of retrieval algorithms in rainfall estimation techniques of PERSIANN family of rainfall products for the tropical region.
Open Access
Artificial neural network to estimate the paddy yield prediction using climatic data
(Hindawi, 2020-07) Amaratunga, V; Wickramasinghe, L; Perera, A; Jayasinghe, J; Rathnayake, U. S
Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.
Open Access
Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka
(Institution of Engineers, Sri Lanka, 2020-07) Laksiri, K; Rathnayake, U. S; Dabare, G; Gunathilake, M. B; Miguntanna, N
Climate variation is a challenging scenario on water resources. Therefore, runoffbased hydropower development stations are at an alarming situation across the world and the hydropower industry has significantly been affected. Therefore, it would be interesting to understand the impact of climate change on hydropower development in a country, where a significant energy contribution takes place by the renewable hydropower. However, such studies in Sri Lanka are limited mainly due to data scarcity. Nevertheless, this study was carried out to understand the relationships between the rainfall and the hydropower development in one of the major hydropower developments in Sri Lanka, Samanalawewa hydropower station. Non-parametric statistical trend analyses were carried out to the monthly rainfall over 26 years for the catchment rainfall. As the initial step, the link between rainfall and hydropower development was tested using the Pearson’s correlation coefficient. Interestingly, results revealed positive rainfall trends over the catchment. The correlation coefficient suggests that there is an acceptable correlation between the rainfall and the hydropower development. However, non-linear analysis is proposed to achieve more sound conclusions. Initial results revealed that there is no adverse impact to the inflow of the reservoir due to the on-going climate change
Open Access
Comparison of different analyzing techniques in identifying rainfall trends for Colombo, Sri Lanka
(Hindawi, 2020-08) Perera, A; Ranasinghe, T; Gunathilake, M. B; Rathnayake, U. S
Identifying rainfall trends in highly urbanized area is extremely important for various planning and implementation activities, including designing, maintaining and controlling of water distribution networks and sewer networks and mitigating flood damages. However, different available methods in trend analysis may produce comparable and contrasting results. Therefore, this paper presents an attempt in comparing some of the trend analysis methods using one of the highly urbanized areas in Sri Lanka, Colombo. Recorded rainfall data for 10 gauging stations for 30 years were tested using the MannKendall test, Sen’s slope estimator, Spearman’s rho test, and innovative graphical method. Results showcased comparable findings among three trend identification methods. Even though the graphical method is easier, it is advised to use it with a proper statistical method due to its identification difficulties when the data scatter has some outliers. Nevertheless, it was found herein that Colombo is under a downward rainfall trend in the month of July where the area receives its major rainfall events. In addition, the area has several upward rainfall trends over the minor seasons and in the annual scale. Therefore, the water management activities in the area have to be revisited for a sustainable use of water resources.
Open Access
Comparison of statistical methods to graphical methods in rainfall trend analysis – case studies from tropical catchments
(https://www.hindawi.com/journals/amete/2019/8603586/, 2019-09-02) Rathnayake, U. S
Time series analyses for climatic factors are important in climate predictions. Rainfall is being one of the most important climatic factors in today’s concern for future predictions; thus, many researchers analyze the data series for identifying potential rainfall trends. The literature shows several methods in identifying rainfall trends. However, statistical trend analysis using Mann–Kendall equation and graphical trend analysis are the two widely used and simplest tests in trend analysis. Nevertheless, there are few studies in comparing various methods in the trend analysis to suggest the simplest methods in analyzing rainfall trends. Therefore, this paper presents a comparison analysis of statistical and graphical trend analysis techniques for two tropical catchments in Sri Lanka. Results reveal that, in general, both trend analysis techniques produce comparable results in identifying rainfall trends for different time steps including annual, seasonal, and monthly rainfalls.
Open Access
Comparison of statistical, graphical and wavelet transform analyses for rainfall trends and patterns in Badulu Oya catchment Sri Lanka
(Hindawi, 2020-09) Ruwangika, A. M; Perera, A; Rathnayake, U. S
Climate change has adversely influenced many activities. It has increased the intensified precipitation events in some places and decreased the precipitation in some other places. In addition, some research studies revealed that the climate change has moved seasons in the temporal scale. Therefore, the changes can be seen in both spatial and temporal scales. Thus, analyzing climate change in the localized environments is highly essential. Rainfall trend analysis in a localized catchment can improve many aspects of water resource management not only to the catchment itself but also to some of the related other catchments. This research is carried to identify the rainfall trends in Badulu Oya catchment, Sri Lanka. The catchment is important as it is in the intermediate climate zone and rich in agricultural productions. Four rain gauges (namely, Badulla, Kandekatiya, Lower Spring Valley, and Ledgerwatte Estate) were used to analyze the rainfalls in the resolutions of monthly, seasonally, and annually. 30-year monthly cumulative rainfall data for the above four gauging stations are analyzed using various standard tests. Nonparametric tests including Mann–Kendall test and sequential Mann–Kendall test and innovative trend analysis methods are used to identify the potential rainfall trends in Badulu Oya catchment. In addition, continuous wavelet transforms and discrete wavelet transforms tests are carried out to check the patterns on rainfall to the catchment. The trend analysis methods are compared against each other to identify the better technique. The results reveal that the nonparametric Mann–Kendall test is powerful to produce the statistically significant rainfall trends in qualitative and quantitative manner. Mann–Kendall analysis shows a positive trend to Ledgerwatte Estate in monthly (3.7 mm in February and 7.4 mm in October), seasonal (6.9 mm in the 2ndintermonsoon), and annual (3 mm annually) scales. However, the analysis records one decreasing rainfall trend to Kandekatiya (8.1 mm in December) only in monthly scale. Nevertheless, it was found that the graphical method can be easily used in qualitative analysis, while discrete wavelet transformations are efficient in identifying the rainfall patterns effectively.
Open Access
Development of wind power prediction models for Pawan Danavi wind farm in Sri Lanka
(Hindawi, 2021-05) Peiris, A. T; Jayasinghe, J. M. J. W.; Rathnayake, U. S
This paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. The monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. The correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. The impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.
Embargo
Diversity and distribution of fauna of the Nasese Shore, Suva, Fiji Islands with reference to existing threats to the biota
(Elsevier, 2016-03-30) Suratissa, D. M; Rathnayake, U. S
Faunal diversity and distribution in the Nasese Shore, Suva, Fiji Islands were studied April–August 2014. The belt transect method was employed to study the species richness and abundance of the fauna. Opportunistic observations were performed to supplement the species richness of the selected habitat types: sandy, rocky and muddy (SRM; Habitat 1); mangrove and sandy (MNS; Habitat 2); muddy and sandy (MS; Habitat 3); and rocky and coral (RC; Habitat 4). Sampling was performed during high and low tide. Faunal density was highest in the RC substrate. The density of mud skippers was significantly higher in the MNS habitat than in the other habitats. This findings could well indicate the environmental pollution levels of this habitat. The Shanon–Weiner Index indicated that the RC habitat possesses the highest diversity, whereas the MS habitat possesses the lowest diversity. In addition, major threats to the biota existed.
Open Access
Ecosystem-Based Adaptation for the Impact of Climate Change and Variation in the Water Management Sector of Sri Lanka
(Hindawi, 2021-02-25) Khaniya, B; Gunathilake, M. B; Rathnayake, U. S
The climate of Sri Lanka has been fluctuating at an alarming rate during the recent past. These changes are reported to have pronounced impacts on the livelihoods of the people in the country. Water is central to the sustainable functioning of ecosystems and wellbeing of mankind. It is evident that pronounced variations in the climate will negatively impact the availability and the quality of water resources. The ecosystem-based adaptation (EbA) approach has proved to be an effective strategy to address the impact of climate change on water resources in many parts of the world. The key aim of this paper is to elaborate the wide range of benefits received through implementation of EbAs in field level, watershed scale, and urban and coastal environments in the context of Sri Lanka. In addition, this paper discusses the benefits of utilizing EbA solutions over grey infrastructure-based solutions to address the issues related to water management. The wide range of benefits received through implementation of EbAs can be broadly classified into three categories: water supply regulation, water quality regulation, and moderation of extreme events. This paper recommends the utilization of EbAs over grey infrastructure-based solutions in adaptation to climate change in the water management sector for the developing region due its cost effectiveness, ecofriendliness, and multiple benefits received on long-term scales. The findings of this study will unequivocally contribute to filling existing knowledge and research gaps in the context of EbAs to future climate change in Sri Lanka. The suggestions and opinions of this study can be taken into account by decision makers and water resources planning agencies for future planning of actions related to climate change adaptation in Sri Lanka.
Open Access
Effect of the entrance zone on the trapping efficiency of desilting tanks in run-of-river hydropower plants
(Department of Civil Engineering, University of Peradeniya, Peradeniya, 2007-10-22) Weerakoon, S. B; Rathnayake, U. S
Run-of-river mini hydropower plants are generally installed in mountainous streams where the catchments are generally steep and vulnerable to high soil erosion. Seasonal heavy rains, especially in tropics and monsoon regions produce large sediment yield from these catchments and the streams experience high sediment concentrations during seasonal floods. Therefore, removal of sand entering into headrace canal in run-of-river mini hydropower plants is an important issue in the run-f-river mini hydropower schemes to reduce the erosion of turbines and other components in contact with water. The desilting tanks constructed in series with the headrace canal play an important role here. The shape and the size of the desilting tank are major factors on the sand trapping efficiency of it. This paper presents a series of laboratory experiments carried out to investigate the effect of the entrance zone on the sand trapping efficiency of the desilting tanks using a scale model of a desilting tank with varying entrance expansion angles. The sand trapping efficiency is found to vary from 50% to 85% with the reduction of espansion angle from 30o to 10o .
Embargo
Enhanced water quality modelling for optimal control of drainage systems under SWMM constraint handling approach
(IOS Press, 2015-01-01) Rathnayake, U. S
Phosphorus and nitrogen are two important nutrients to plants. Therefore, fertilizers usually used in agricultural lands hold a significant amount of phosphorus and nitrogen. Even though these two are essential for plants, they are treated as pollutants when they are contaminated to the fresh waters. Therefore, phosphorus in stormwater runoff is a concerned topic for combined sewer overflows (CSOs). Rathnayake and Tanyimboh's optimal control model was capable of handling five different water quality parameters (chemical oxygen demand, bio-chemical oxygen demand, total suspended solids, total Kjeldhal nitrogen and nitrates and nitrites) in CSOs. However, the enhanced approach is capable of integrating phosphorus concentrations into the analysis of water quality from CSOs. The new optimal control model for drainage systems was run and compared against the previous work by the author. Promising findings are illustrated from the newly developed model in controlling drainage systems.
Open Access
Entrance zone effect on the sediment trapping efficiency in desilting tanks of run-of-river type mini-hydropower plants
(The University of Peradeniya, Peradeniya, Sri Lanka, 2007-11-30) Rathnayake, U. S; Harishchandra, M. R. T. S; Weerakoon, S. B
Development of run-of-river type minihydropower plants is receiving increased attention in Sri Lanka at present owing to the incentives announced for developers of renewable power generation projects during the last decade by the Sri Lankan government. The sources for most of the run-of-river type mini hydropower plants are mountainous streams where the discharges experience significant seasonal variation with frequent flash floods. The catchments of these streams are generally steep and face an increasing trend of soil erosion due to cultivation and other human activities. Therefore, the stream flows carry high sediment loads during seasonal floods. This sediment-laden flow enters the headrace canals feeding water to the turbines of the rninhydropower plants. Sediment in the water passing through the turbines with high velocity erode the contact surfaces of turbine components. The erosion of turbine components leads to a drop in hydraulic efficiency and to a high maintenance cost of the turbines. Removal of sand carried with the flow in the headrace canals of run-of-river mini hydropower plants is therefore an important issue for the developers to reduce the maintenance cost of the turbines (Singal and Ranendra, 2006). Introduction of a de-silting tank in series with the headrace canal is one of the commonly used techniques for this purpose. De-silting tanks are designed as settling basins to settle sediment greater than a targeted size (Janssen, 2004). The shape and the size of the de-silting tank are major factors affecting the sand trapping efficiency of the desilting tank. Several empirical and semiempirical relations for the efficiency of …
Open Access
Environmental and Social Impacts of Mini-hydropower Plants—A Case Study from Sri Lanka
(DAVID PUBLISHING, 2018-03-15) Senarath, P.; Khaniya, B; Baduge, N; Azamathulla, H. M; Rathnayake, U. S
This research study was conducted to review the environmental and social impact of mini hydropower plants (run-of-the-river type) by selecting Denawaka Ganga mini hydropower plant, which is located in Ratnapura district, Sri Lanka. Field visits and discussions among the authors, authorities and the residents were carried out. Then, the environmental and social impacts were scientifically analysed using regulation degree (RD) and environmental impact value (EIV) scores. It was found out that the Denawaka Ganga mini hydropower plant has induced some environmental concerns; however, significant positive social impact to the society. This is in addition to the green energy generation. Therefore, it can be concluded herein that the Denawaka Ganga mini hydropower is an asset to the country, Sri Lanka.
Unknown
Evaluation of future climate and potential impact on streamflow in the Upper Nan River basin of Northern Thailand
(Hindawi, 2020-10) Rathnayake, U. S; Gunathilake, M. B; Amaratunga, V; Perera, A
Water resources in Northern Thailand have been less explored with regard to the impact on hydrology that the future climate would have. For this study, three regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment (CORDEX) of Coupled Model Intercomparison Project 5 (CMIP5) were used to project future climate of the upper Nan River basin. Future climate data of ACCESS_CCAM, MPI_ESM_CCAM, and CNRM_CCAM under Representation Concentration Pathways RCP4.5 and RCP8.5 were bias-corrected by the linear scaling method and subsequently drove the Hydrological Engineering Center-Hydrological Modeling System (HEC-HMS) to simulate future streamflow. This study compared baseline (1988–2005) climate and streamflow values with future time scales during 2020–2039 (2030s), 2040–2069 (2050s), and 2070–2099 (2080s). The upper Nan River basin will become warmer in future with highest increases in the maximum temperature of 3.8°C/year for MPI_ESM and minimum temperature of 3.6°C/year for ACCESS_CCAM under RCP8.5 during 2080s. The magnitude of changes and directions in mean monthly precipitation varies, with the highest increase of 109 mm for ACESSS_CCAM under RCP 4.5 in September and highest decrease of 77 mm in July for CNRM, during 2080s. Average of RCM combinations shows that decreases will be in ranges of −5.5 to −48.9% for annual flows, −31 to −47% for rainy season flows, and −47 to −67% for winter season flows. Increases in summer seasonal flows will be between 14 and 58%. Projection of future temperature levels indicates that higher increases will be during the latter part of the 20th century, and in general, the increases in the minimum temperature will be higher than those in the maximum temperature. The results of this study will be useful for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset negative impacts of future changes in climate. In addition, the results will also be valuable for agriculturists and hydropower planners.
Unknown
Experimental investigation of hyporheic interactions
(2010) Rathnayake, U. S; Izumi, N
Research on hyporheic interactions is not new to the present world, but most of the previous research is in the environmental and ecological points of view. This study was to understand the hyporheic interactions by means of engineering perspectives. Several experiments were carried out at laboratory scale to identify the relationships between important non-dimensional river parameters and non-dimensional interaction parameters. Results can be concluded to show some clear relationships among the non-dimensional parameters.
Unknown
Flood modeling in the Mahaweli River reach from Kothmale to Polgolla
(University of Peradeniya, 2007) Rathnayake, U. S; Weerakoon, S. B; Nandalal, K. D. W; Rathnayake, U
The occurrence of floods and inundation of the low lands adjacent to the Mahaweli River reach from Gampola to Polgolla were very frequent prior to the Kotmale reservoir project in mid 1980s. However, during last two decades with the construction of the Kotmale dam, the regulation of flow by the reservoir has reduced the inundation risk of these lands, which were vulnerable to frequent flooding. As a result, these lands are developed at an increasing rate and more people have started to live in them. This fact gives an alarming signal to the authorities, as the damage that might be caused due to an extreme flood event could be significant. It is therefore of paramount importance that comprehensive flood modeling and inundation analysis of the Mahaweli River reach between Kotmale and Polgolla is carried out. This paper presents the flood modeling and inundation analysis in the Mahaweli river reach from the Kotmale dam to Polgolla barrage using the HECRAS model. The HECRAS model was set up for the river reach using the river cross-sections at 200 m intervals from Kotmale dam to Polgolla barrage. The model was applied to estimate the water stages along the river reach for the floods of different return periods. Though the Kotmale reservoir acts as a flood control reservoir for floods of medium return periods, it becomes ineffective to reduce the flood levels in the downstream flood plains due to floods of high return periods when it has to release high discharge. Inundation areas in the downstream of the dam due to several flood discharges are presented.
Open Access
Flood Modelling in Waidina Tributary, Fiji Islands
(2015 Department of Civil and Environmental Engineering, University of Ruhuna3rd International Symposium on Advances in Civil and Environmental Engineering, 2015) Rathnayake, U. S; Arachchi, S. M. A
Rewa River is the widest river and the second longest river in Fiji Islands. It is in the islands of Viti Levu. The river is 145 km in length and starts from the highest peak of the island, Tomanivi. Rewa River experiences frequent floods. However, a detailed flood model for Rewa River is still to be tabled. This paper briefs the flood modeling of part of Waidina tributary of the Rewa River. US Army Corps Engineers HEC-RAS hydraulic model is being successfully applied to the Rewa River and initial results are drafted for the Waidina tributary. These results are promising; however, a completed flood model should be developed for sound conclusions. Inundation top widths due to a random flood were presented. These inundations can effectively be used to inform the residents in the vulnerable areas in a flood event. In addition, the final expected results can be used for the flood protection structural measures.
Open Access
Forecasting Wind Power Generation using Artificial Neural Network: “Pawan Danawi” - A Case Study from Sri Lanka
(Hindawi, 2021-03) Pereis, A. T; Jayasinghe, J. M. J. W; Rathnayake, U. S
Under the background of the global integrated supply chain, the work of logistics is more and more complicated. Warehouse management is now an important part of logistics. The optimization of the logistics tracking system in the building material market proves that the tracking result of the system is highly reliable. The system has the advantages of small size, low cost, accurate positioning, real-time convergence, and high performance.
Open Access
Gene expression programming and artificial neural network to estimate atmospheric temperature in Tabuk, Saudi Arabia
(SpringerLink & King Abdulaziz City for Science and Technology, 2018-09-19) Azamathulla, H. M; Rathnayake, U. S; Shatnawi, A
Climate change is not a myth. There is enough evidence to showcase the impact of climate change. Town planners and authorities are looking for potential models to predict the climatic factors in advance. Being an agricultural area in Saudi Arabia, Tabuk region gets greater interest in developing such a model to predict the atmospheric temperature.Therefore, this paper presents two diferent studies based on artifcial neural networks (ANNs) and gene expression programming (GEP) to predict the atmospheric temperature in Tabuk. Atmospheric pressure, rainfall, relative humidity and wind speed are used as the input variables in the developed models. Multilayer perceptron neural network model (ANN model), which is high in precession in producing results, is selected for this study. The GEP model that is based on evolutionary algorithms also produces highly accurate results in nonlinear models. However, the results show that the GEP model outperforms the ANN model in predicting atmospheric temperature in Tabuk region. The developed GEP-based model can be used by the town and country planers and agricultural personals