Scopus Index Publications

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162

This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

Browse

Filters

2022 - 20233

Advanced Search

Filter by

Settings

Author: search.filters.author.Sarukkalige, R

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen Access
    Wetland Water Level Prediction Using Artificial Neural Networks—A Case Study in the Colombo Flood Detention Area, Sri Lanka
    (MDPI, 2023-01) Jayathilake, T; Sarukkalige, R; Hoshino, Y; Rathnayake, U
    Historically, wetlands have not been given much attention in terms of their value due to the general public being unaware. Nevertheless, wetlands are still threatened by many anthropogenic activities, in addition to ongoing climate change. With these recent developments, water level prediction of wetlands has become an important task in order to identify potential environmental damage and for the sustainable management of wetlands. Therefore, this study identified a reliable neural network model by which to predict wetland water levels over the Colombo flood detention area, Sri Lanka. This is the first study conducted using machine learning techniques in wetland water level predictions in Sri Lanka. The model was developed with independent meteorological variables, including rainfall, evaporation, temperature, relative humidity, and wind speed. The water levels measurements of previous years were used as dependent variables, and the analysis was based on a seasonal timescale. Two neural network training algorithms, the Levenberg Marquardt algorithm (LM) and the Scaled Conjugate algorithm (SG), were used to model the nonlinear relationship, while the Mean Squared Error (MSE) and Coefficient of Correlation (CC) were used as the performance indices by which to understand the robustness of the model. In addition, uncertainty analysis was carried out using d-factor simulations. The performance indicators showed that the LM algorithm produced better results by which to model the wetland water level ahead of the SC algorithm, with a mean squared error of 0.0002 and a coefficient of correlation of 0.99. In addition, the computational efficiencies were excellent in the LM algorithm compared to the SC algorithm in terms of the prediction of water levels. LM showcased 3–5 epochs, whereas SC showcased 34–50 epochs of computational efficiencies for all four seasonal predictions. However, the d-factor showcased that the results were not within the cluster of uncertainty. Therefore, the overall results suggest that the Artificial Neural Network can be successfully used to predict the wetland water levels, which is immensely important in the management and conservation of the wetlands
  • Thumbnail Image
    PublicationOpen Access
    Water Footprint Assessment for Irrigated Paddy Cultivation in Walawe Irrigation Scheme, Sri Lanka
    (MDPI, 2022-11-25) Janani, H. K; Abeysiriwardana, H. D; Rathnayake, U; Sarukkalige, R
    Water footprint (WF) is a comprehensive summation of the volume of freshwater consumed directly and indirectly in all the steps of the production chain of a product. The water footprint concept has been widely used in agricultural water resources management. Water for irrigation is supplied in Sri Lanka to farmers at no cost, and thus the question is arising, whether the current management strategies the authorities and the farmers follow are appropriate to achieve productive water utilization. Therefore, this study aims at evaluating the water footprint of rice production in an irrigation scheme in the dry zone of Sri Lanka, the Walawe irrigation scheme. Due to the unreliability of the rainfall in the study area paddy cultivation depends entirely on irrigation, thus, the (Formula presented.), in other terms the volume of water evaporated from the irrigation water supply is considered as the total WF ( (Formula presented.) in this study. Actual crop evapotranspiration (equivalent to (Formula presented.)) was estimated based on the Penman-Monteith (P-M) model integrating effective rainfall, and crop coefficient published in Sri Lankan Irrigation Design Guidelines. The study spanned for three irrigation years from 2018–2021. Actual irrigation water issued to the field was estimated based on the data recorded by the government body responsible for irrigation water management of the area—Mahaweli Authority of Sri Lanka. The total volume of percolated water was computed employing the water balance method while assuming runoff is negligible. Results show that the average annual (Formula presented.) found to be 2.27 m3/kg, which is higher than global and national (Formula presented.). As the crop yield in the study area (6.5 ton/ha) is also higher than the global (4.49 ton/ha) and national (3.5 ton/ha) yields, a conclusion was drawn that the irrigation water usage ( (Formula presented.) in the area may be significantly higher. It was then noted the higher (Formula presented.) was due to relatively higher evapotranspiration in the area. Thus, it is vital to reduce excess water usage by shifting irrigation practices from flooded irrigation to the System of Rice Intensification (SRI). © 2022 by the authors.
  • Thumbnail Image
    PublicationOpen Access
    Application of GIS Techniques in Identifying Artificial Groundwater Recharging Zones in Arid Regions: A Case Study in Tissamaharama, Sri Lanka
    (MDPI, 2022-12-10) Kariyawasam, T; Basnayake, V; Wanniarachchi, S; Sarukkalige, R; Rathnayake, U
    Groundwater resources are severely threatened not only in terms of their quality but also their quantity. The availability of groundwater in arid regions is highly important as it caters to domestic needs, irrigation, and industrial purposes in those areas. With the increasing population and human needs, artificial recharging of groundwater has become an important topic because of rainfall scarcity, high evaporation, and shortage of surface water resources in arid regions. However, this has been given the minimum attention in the context of Sri Lanka. Therefore, the current research was carried out to demarcate suitable sites for the artificial recharging of aquifers with the help of geographic information system (GIS) techniques, in one of the water-scarce regions in Sri Lanka. Tissamaharama District Secretariat Division (DSD) is located in Hambanthota district. This region faces periodic water stress with a low-intensity seasonal rainfall pattern and a lack of surface water sources. Hydrological, geological, and geomorphological parameters such as rainfall, soil type, slope, drainage density, and land use patterns were considered to be the most influential parameters in determining the artificial recharging potential in the study area. The GIS tools were used to carry out a weighted overlay analysis to integrate the effects of each parameter into the potential for artificial groundwater recharge. The result of the study shows that 14.60% of the area in the Tissamaharama DSD has a very good potential for artificial groundwater recharge, while 41.32% has a good potential and 39.03% and 5.05% have poor and very poor potential for artificial groundwater recharge, respectively. The southern part of the DSD and the Yala nature reserve areas are observed to have a higher potential for artificial groundwater recharge than the other areas of Tissamaharama DSD. It is recommended to test the efficiency and effects of groundwater recharge using groundwater models by simulating the effects of groundwater recharge in future studies. Therefore, the results of the current research will be helpful in effectively managing the groundwater resources in the study area.