Research Publications Authored by SLIIT Staff

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195

This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

Browse

Filters

2018 - 201912020 - 20233

Advanced Search

Filter by

Settings

Subject: search.filters.subject.wetlands

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    PublicationOpen Access
    Wetland Water-Level Prediction in the Context of Machine-Learning Techniques: Where Do We Stand?
    (MDPI, 2023-05) Jayathilake, T; Gunathilake, M. B; Wimalasiri, E.M; Rathnayake, U
    Wetlands are simply areas that are fully or partially saturated with water. Not much attention has been given to wetlands in the past, due to the unawareness of their value to the general public. However, wetlands have numerous hydrological, ecological, and social values. They play an important role in interactions among soil, water, plants, and animals. The rich biodiversity in the vicinity of wetlands makes them invaluable. Therefore, the conservation of wetlands is highly important in today’s world. Many anthropogenic activities damage wetlands. Climate change has adversely impacted wetlands and their biodiversity. The shrinking of wetland areas and reducing wetland water levels can therefore be frequently seen. However, the opposite can be seen during stormy seasons. Since wetlands have permissible water levels, the prediction of wetland water levels is important. Flooding and many other severe environmental damage can happen when these water levels are exceeded. Therefore, the prediction of wetland water level is an important task to identify potential environmental damage. However, the monitoring of water levels in wetlands all over the world has been limited due to many difficulties. A Scopus-based search and a bibliometric analysis showcased the limited research work that has been carried out in the prediction of wetland water level using machine-learning techniques. Therefore, there is a clear need to assess what is available in the literature and then present it in a comprehensive review. Therefore, this review paper focuses on the state of the art of water-level prediction techniques of wetlands using machine-learning techniques. Nonlinear climatic parameters such as precipitation, evaporation, and inflows are some of the main factors deciding water levels; therefore, identifying the relationships between these parameters is complex. Therefore, machine-learning techniques are widely used to present nonlinear relationships and to predict water levels. The state-of-the-art literature summarizes that artificial neural networks (ANNs) are some of the most effective tools in wetland water-level prediction. This review can be effectively used in any future research work on wetland water-level prediction. © 2023 by the authors.
  • Thumbnail Image
    PublicationOpen Access
    Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka
    (MDPI, 2023-04-02) Herath, M; Jayathilaka, T; Azamathulla, H.M; Mandala, V; Rathnayake, N; Rathnayake, U
    Wetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall–nighttime relative humidity, rainfall–evaporation, daytime relative humidity–evaporation, and rainfall–nighttime relative humidity–evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.
  • Thumbnail Image
    PublicationOpen Access
    Deep Machine Learning-Based Water Level Prediction Model for Colombo Flood Detention Area
    (MDPI, 2023-02-08) Herath, M; Jayathilaka, T; Hoshino, Y; Rathnayake, U
    Machine learning has already been proven as a powerful state-of-the-art technique for many non-linear applications, including environmental changes and climate predictions. Wetlands are among some of the most challenging and complex ecosystems for water level predictions. Wetland water level prediction is vital, as wetlands have their own permissible water levels. Exceeding these water levels can cause flooding and other severe environmental damage. On the other hand, the biodiversity of the wetlands is threatened by the sudden fluctuation of water levels. Hence, early prediction of water levels benefits in mitigating most of such environmental damage. However, monitoring and predicting the water levels in wetlands worldwide have been limited owing to various constraints. This study presents the first-ever application of deep machine-learning techniques (deep neural networks) to predict the water level in an urban wetland in Sri Lanka located in its capital. Moreover, for the first time in water level prediction, it investigates two types of relationships: the traditional relationship between water levels and environmental factors, including temperature, humidity, wind speed, and evaporation, and the temporal relationship between daily water levels. Two types of low load artificial neural networks (ANNs) were developed and employed to analyze two relationships which are feed forward neural networks (FFNN) and long short-term memory (LSTM) neural networks, to conduct the comparison on an unbiased common ground. The LSTM has outperformed FFNN and confirmed that the temporal relationship is much more robust in predicting wetland water levels than the traditional relationship. Further, the study identified interesting relationships between prediction accuracy, data volume, ANN type, and degree of information extraction embedded in wetland data. The LSTM neural networks (NN) has achieved substantial performance, including R2 of 0.8786, mean squared error (MSE) of 0.0004, and mean absolute error (MAE) of 0.0155 compared to existing studies.
  • Thumbnail Image
    PublicationOpen Access
    ESTIMATING WILLINNESS TO PAY FOR WETLAND CONSERVATION: A CONTINGENT VALUATION STUDY OF MUDUN ELA AND KALU OYA WATERSHEDS, WESTERN PROVINCE SRI LANKA
    (International Conference on "Water, Environment and Climate Change: Knowledge Sharing and Partnership, 2018-04-10) Jayathilaka, R; Serasinghe, P
    Wetland ecosystems are often neglected or undervalued. Few people realize the range of products derived from wetlands and their freshwater habitats. There are a number of social and economic benefits of wetlands and the wetlands are in a serious risk today. Specially, in relation to human activities and in inconvenient utilization, wetlands around the globe are being modified, reclaimed and over-exploited due to high levels ofresource consumption, land conversion and also upstream developments that alter the quality and flow of water that feeds into them. Decision makers often have a little understanding of the environmental value of wetlands because wetlands are often perceived as having little or no value compared with uses that yield more visible and immediate economic benefits. In this circumstances, revelation and recognition of value of wetlands and thereafter the valuation their importance has been an essential matter which helps to protect such habitats and also it is useful to utilize the benefits of wetlands in a proper management. The objective of this study is to estimate the willingness to pay (WTP) for conservation of MudunEla and KaluOya watersheds, Western province Sri Lanka using Contingent Valuation Method (CVM). Respondents were randomly selected for data collection through face-to-face interview. The Tobit model was used to estimate the entrance fee to for conservation. The marginal effects on probabilities in the tobit model suggest that postgraduate degree holders, households who are using wetland as education, research and agricultural purposes play significant roles in residents' WTP for protect wetland. Thus, higher educated households was found to have a positive response on willingness to pay for protect wetlands in the country. On the other hand, households who were used wetland for agricultural purposes have a negative response on willingness to pay for wetland protect practices.