Browsing by Author "Perera, A"

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Addressing Child Labour in SAARC: The Synergy of Education, Health and Economic Growth Towards SDGs
(John Wiley and Sons, 2025-11-09) Muthugala, H; Magammana, T; Perera, A; Bandara, A; Jayathilaka, R
Child labour remains a critical socio-economic challenge in the SAARC region, closely linked to sustainable development goals (SDGs). This study investigates the determinants of child labour by examining the roles of education, health and economic growth using a robust methodological framework. The analysis captures the non-linear country-specific relationships between these variables and child labour, employing advanced methodological approaches, including multiple polynomials, stepwise and simple polynomial regression. The findings reveal a complex interplay of factors, with each variable showing positive and negative effects on child labour in country-specific contexts. Improved access to education generally reduces child labour, but disparities in quality and affordability can have the opposite effect. Health improvements significantly lower child labour rates, yet unequal healthcare access perpetuates exploitation among vulnerable groups. Economic growth shows dual effects: it promotes adult employment and alleviates poverty, yet unregulated expansion in specific sectors can heighten the demand for child labour. This study makes a novel contribution by integrating socio-economic determinants with child labour within a regional framework, providing actionable insights while aligning with SDGs 3, 4, 8 and 8.7. Key policy recommendations include fostering regional collaboration, ensuring access to free education, enacting and enforcing new laws, improving healthcare infrastructure and promoting inclusive and sustainable economic growth. These measures align with global SDG commitments but aim to secure a brighter future for the region's children by achieving these goals by 2030.
Embargo
Ai based greenhouse farming support system with robotic monitoring
(IEEE, 2020-11-16) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. W
Greenhouses plays a major role in today's agriculture since farmers can grow plants under controlled climatic conditions and can optimize production. The greenhouses are usually built in areas where the climatic conditions for the growth of plants are not optimal so requires some artificial setups to bring about productivity. Automating process of a greenhouse requires monitoring and controlling of the climatic parameters. This paper is an attempt to minimize the cost of maintaining greenhouse environments using new technologies. The end goal of this research an automated system to optimally monitor and control the environmental factors inside greenhouse by monitoring temperature, soil moisture, humidity and pH through a cloud connected mobile robot which can detect unhealthy plants using image processing and machine learning. The mobile robot navigates through a predefined map of greenhouse. Database server has created to store gathered real-time data. And the necessary accurate data represent by using proper application for analyzing.
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
Breaking the cycle: long-term socio economic determinants of child labour in SAARC countries
(BioMed Central Ltd, 2025-11-19) Magammana, T; Muthugala, H; Bandara, A; Perera, A; Jayathilaka, R
Background: Child labour remains a critical issue in SAARC countries, driven by various socio-economic factors. While previous studies have explored individual determinants, limited research has been conducted on their collective long-term impact. Understanding how structural and economic conditions shape child labour trends is essential for designing effective policy interventions. Methods: This study engages panel cointegration techniques to examine the long-term relationship between child labour and key socio-economic drivers in SAARC countries. It assesses the impact of education, access to healthcare, economic conditions, labour market dynamics, foreign investment, and urbanisation on the prevalence of child labour. Results: The findings confirm a stable, long-term relationship between child labour and these determinants in each SAARC country. Improvements in education and health significantly reduce child labour. However, economic growth and urbanisation have complex, country-specific effects. Higher unemployment and increased FDI may also influence child labour, emphasising the need for targeted policy responses. Conclusions: The study highlights the significance of ongoing investments in education and healthcare. Labour market reforms are crucial to mitigate the impact of unemployment, while inclusive economic policies ensure that growth benefits vulnerable populations. Targeted strategies for FDI and urbanisation are necessary to prevent unintended consequences on child labour. Combating child labour in SAARC countries requires a multi-sectoral approach. Regional collaboration is crucial for sharing best practices, developing unified strategies, and enhancing cross-border initiatives. Holistic policies integrating education, health, and economic planning are key to reducing child labour.
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 different Artificial Neural Network (ANN) training algorithms to predict atmospheric temperature in Tabuk, Saudi Arabia
(researchgate.net, 2020-06) Perera, A; Azamathulla, H; Rathnayake, U
Use of Artificial neural network (ANN) models to predict weather parameters has become important over the years. ANN models give more accurate results in weather and climate forecasting among many other methods. However, different models require different data and these data have to be handled accordingly, but carefully. In addition, most of these data are from non-linear processes and therefore, the prediction models are usually complex. Nevertheless, neural networks perform well for non-linear data and produce well acceptable results. Therefore, this study was carried out to compare different ANN models to predict the minimum atmospheric temperature and maximum atmospheric temperature in Tabuk, Saudi Arabia. ANN models were trained using eight different training algorithms. BFGS Quasi Newton (BFG), Conjugate gradient with Powell-Beale restarts (CGB), Levenberg-Marquadt (LM), Scaled Conjugate Gradient (SCG), Fletcher-Reeves update Conjugate Gradient algorithm (CGF), One Step Secant (OSS), Polak-Ribiere update Conjugate Gradient (CGP) and Resilient Back-Propagation (RP) training algorithms were fed to the climatic data in Tabuk, Saudi Arabia. The performance of the different training algorithms to train ANN models were evaluated using Mean Squared Error (MSE) and correlation coefficient (R). The evaluation shows that training algorithms BFG, LM and SCG have outperformed others while OSS training algorithm has the lowest performance in comparison to other algorithms used.
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
CORPORATE GOVERNANCE AND FIRM INTEGRATED PERFORMANCE: A CONCEPTUAL FRAMEWORK
(researchgate.net, 2022-05-09) Nagalingam, N; Kumarapperuma, C; Malinga, C; Gayanthika, K; Amanda, N; Perera, A
Though the corporate governance has been studied from the viewpoint of first, accounting and financial performance (Khatib & Nour, 2021; Goel, 2018; Mohamed, Basuony, & Badawi, 2013), next, marketing performance (El Fawal & Mawlawi, 2018), and finally, logistic and supply chain performance (Hernawati & Surya, 2019) in isolation, moreover, literature on the first is comparatively higher than on the other two, it is further argued that it has not been studied from the viewpoint of firm integrated performance. The purpose of this study, therefore, is to conceptualize the relationship between corporate governance and firm integrated performance. The study adopted a rigorous literature review in forming critical arguments for the theme studied. Accordingly, the study embraced rigorous a priori knowledge in building the arguments for hypotheses development. The study proposes a conceptual framework for the relationship between corporate governance and firm integrated performance which has the potential of facilitating efficient decision-making on corporate governance and firm integrated performance. The study concludes with a foundation for the theoretical basis of the relationship between corporate governance and firm integrated performance.
Open Access
An empirical study of students’ satisfaction with professional accounting education programs, Sri Lanka
(researchgate.net, 2020-07-29) Nadishana, G. S. W. Y; Ameen, Z; Kulatunga, K. A; Perera, A; Perera, C; Madhavika, W. D. N; Nagendrakumar, N
This study aims to analyze the factors affecting students' satisfaction with professional accounting courses offered by Professional Accounting Education Institutions, and then aims to assess the impact of students' satisfaction and students' loyalty towards Professional Accounting Education Institutions in Sri Lanka. It is evident that a significant gap exists between student enrolment and the rate of students’ passing out as professional accountants as per the annual reports of the Institute of Chartered Accountants of Sri Lanka and the Institute of Certified Management Accountants of Sri Lanka (2014-2018). The study adopted a deductive methodology while employing a stratified random sampling technique and distributed 500 questionnaires which had a response rate of 80%. The data was analyzed using structural equation modeling via SPSS and AMOS versions 25. The study concludes that course assessment and institutional image, teaching methods, teaching staff, course organization and infrastructure facilities, and institutional administration and efficiency significantly impact the student satisfaction. And also, it concludes that the students’ satisfaction significantly impacts students’ loyalty. This study add value to the literature by focusing the students’ satisfaction from two extreme angles (i.e., students’ need and loyalty) and introduces a new model which would enhance the appropriate administration of the Professional Accounting Education Institutions
Open Access
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.
Embargo
GreenEye: Smart Consulting System for Domestic Farmers
(IEEE, 2022-12-09) Mendis, O; Perera, A; Ranasinghe, S; Chandrasiri, S
Always it is challenging for typical domestic farmers to maintain a good homestead in today’s world and with the ever-growing economic concerns. To save time, money, and energy, they must keep up with the advancements of incorporating technology in their farming practices to ensure that their crops are up to standard and optimized for the maximum yield. Domestic farmers may grow crops for economic gain, pleasure, stress relief, decorative purposes, Etc. However, regardless of the purpose, everyone must be aware of good farming practices. No matter the intention, challenges, and outcomes, everyone engaged with plant growth is the same. In today’s highly advanced technological world, a lot of domestic farmers are using modern technology in their growing practices. Experimenting with intelligent growth mechanisms and intend to use modern technologies to provide advice that is useful for all gardeners who prefer home gardening. Additionally, the most crucial aspects of plant care are recognizing the ideal plants for each season, identifying stress factors, identifying diseases, identifying soil moisture levels, and predicting the harvest based on the current environmental conditions. Green Eye mobile application aims to provide a comprehensive solution to technologized domestic farmers using image processing technologies for their most common concerns.
Open Access
Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka
(Springer International Publishing, 2019-04) Perera, A; Rathnayaka, U. S
Impact of climate change or climate variability on water resources is an exceedingly concerned issue. Hydropower development is one of the most affected industries due to the climatic variability. Therefore, this paper presents the promising results from a study of the impact of climate variability on hydropower generation of Erathna run-of-the-river (ROR) hydropower plant located in Rathnapura district, Sri Lanka. This study was based on surrounded rain gauges outside the catchment as Erathna catchment area is an un-gauged catchment. 30-year rainfall trend analysis from 1988 to 2017 was done using Mann–Kendall and Sen’s slope estimator tests to predict the available trends. Pearson’s correlation coefficient was used to investigate the relationship between rainfall and Erathna power generation. Results show negative trends for annual rainfalls in several rain gauges, while seasonal trend analyses support that observation. July is the most critical month for most of the rain gauges around the catchment. The results also show a good correlation between the rainfalls and power generation. Therefore, the results conclude the importance of rainfall trend analysis in un-gauged catchments and its forecasting capacity of water resources usage in hydropower development.
Embargo
Intelligent disease detection system for greenhouse with a robotic monitoring system
(IEEE, 2020-12-10) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. W
Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.
Embargo
The Next Gen Security Operation Center
(IEEE, 2021-04-02) Perera, A; Rathnayaka, S; Che, C; Madushanka, W. W; Senarathne, A. N
Due to the evolving Cyber threat landscape, Cyber criminals have found new and ingenious ways of breaching defenses in networks. Due to the sheer destruction these threat actors can cause to an organization, most modern-day organizations have focused their attention towards protecting their critical infrastructure and sensitive information through multiple methods. The main defense against both internal and external threats to an organization has been the implementation of the Security Operations Center (SOC) which is responsible for monitoring, analyzing and mitigating incoming threats. At the heart of the Security Operations Center, lies the Security Information and Event Management system (SIEM) which is utilized by SOC analysts as the centralized point where all security notifications from various security technologies including firewalls, IPS/IDS and Anti-Virus logs are collected and visualized. The effective operation of SOC in an organization is dependent on how well the SIEM filters log events and generates actual alerts. Here lies the major problem faced by SOC analysts in detecting threats. If proper alert correlation is not accomplished, analysts would have to deal with too much alert noise due to a high false positive count. This would ultimately cause analysts to miss critical security incidents, thus causing severe implications to the organization's security. The performance of a SIEM can be enhanced through adding various functionalities such as Threat Hunting, Threat Intelligence and malware identification and prevention in order to reduce false positive alarms, threat framework and machine learning which would increase the accuracy and efficiency of the overall Security Operations process of an organization. Even though many products which provide these additional functionalities exist in the current market, they can be too expensive for smaller scale organizations to handle. Our aim is to make security operations deliverable to any organization regardless of the size and scale without any financial implications and enhance its functionalities with the aid of Advanced Machine Learning Techniques.
Open Access
Rainfall and atmospheric temperature against the other climatic factors – Case study from Colombo, Sri Lanka
(2019-12) Perera, A; Rathnayake, U. S
Climate prediction is given a high priority by many countries due to its importance in mitigation of extreme weather conditions. However, the prediction is not an easy task as the climatic parameters not only show spatial variations but also temporal variations. In addition, the climatic parameters are interrelated. To overcome these difficulties, soft computing techniques are widely used in prediction of climate variables with respect to the other variables. On the other hand, Colombo, Sri Lanka, is experiencing adverse or extreme weather conditions over the last few years. However, a climate prediction study is yet to be carried out in this tropical climatic zone. Therefore, this paper presents a study, identifying relationships between the two most impacted climate parameters (atmospheric temperature and rainfall) and other climatic parameters. Artificial neural network (ANN) models are developed to define the relationships and then to predict the atmospheric temperature as a function of other parameters including monthly rainfall, minimum and maximum relative humidity, and average wind speed. Same analysis is carried out to define the prediction model to the monthly rainfall. The best algorithm out of several other ANN algorithms is chosen for the analyses. Results revealed that the atmospheric temperature in Colombo can be presented with respect to the other climatic variables. However, the rainfall does not show a greater relationship with the other climatic parameters.
Open Access
Recent Climatic Trends In Trinidad And Tobago, West Indies
(Research and Technology Transfer Affairs Division,, 2020-02) Perera, A; Mudannyake, S; Azamathulla, H; Rathnayake, U. S
Seawater level rise is one of the most prevalent adverse environmental impacts of the ongoing global warming process. Island nations are more vulnerable to the impact than the land masses. Two such islands impacted by global warming are Trinidad and Tobago, located in the Atlantic Ocean. However, there is minimal related research in this area in the context of the impact of climate variability. Therefore, it is timely and interesting to assess the climatic trends in islands that are extremely vulnerable like Trinidad and Tobago. This paper presents a detailed non-parametric statistical analysis for well-known climate gauges in Trinidad and Tobago, West Indies. Mann Kendall and Sen’s slope tests were carried out on two identified rain gauges in Trinidad and Tobago. Monthly climatic data including cumulative rainfall and the average of the minimum and maximum atmospheric temperatures were processed to identify the trend analysis using the above stated non-parametric tests. Important results are found from the analysis; most importantly, there is no significant impact on the rainfall in the area due to the climate variability over 30 years. However, the atmospheric temperature behaves in a different way and it has a rising pattern across the total 12 months studied. This can be seen for both the minimum and maximum atmospheric temperatures. Therefore, the warm months are becoming warmer and the cold months are becoming less cold. This is a critical finding that must be considered for any future planning processes.
Open Access
Relationships between hydropower generation to rainfall – gauged and un-gauged catchments from Sri Lanka
(hindawi.com, 2020-07) Rathnayake, U. S; Perera, A
The relationship between the rainfall and minihydropower generation in a catchment is highly nonlinear. Therefore, the prediction of minihydropower generation is complex. However, the prediction is important in optimizing the control of electricity generation under various environmental conditions. Ongoing climate variabilities have completely changed the minihydropower generation to some parts of the world, and it is significant. Therefore, this paper presents results from two soft-computing studies in searching the relationships between rainfall and the generated hydropower. The first study was carried out for a gauged catchment; however, the second was carried for an ungauged catchment. Results revealed that there is an acceptable correlation in between the rainfall and hydropower generation for the gauged catchment and a marginal contribution to the ungauged catchment.
Embargo
Social media based personalized advertisement engine
(IEEE, 2018-02-19) De Silva, H; Jayasinghe, P; Perera, A; Pramudith, S; Kasthurirathna, D
Online advertising has become a global phenomenon that affects the retail market substantially. Advertisements engines are an effective solution to the mobile application market to push advertisements. This paper reports evidence that AdSeeker, User Preference Based Advertisement Engine Based on Social Media is an effective solution to improve the business value of the marketing and advertising. Since the internet is used by vast number of people, it essentially needs a comprehensive method to push personalized advertisements to the right people. Adseeker is a system built using ontological mapping and social media content based semantic analysis to direct personalized. Identifying personal relationship hierarchy, and ontological approach for advertisement classification helps to identify the most appropriate advertisement for each user. AdSeeker uses the tweets posted by users to capture the preference of each and every user. Each user pushed advertisements based on their individual preferences. Based on the social experiments done using Adseeker, we could demonstrate that the social media profile based advertising is effective in providing highly relevant advertisements.
Open Access
Statistical evaluation and hydrologic simulation capacity of different satellite-based precipitation products (SbPPs) in the Upper Nan River Basin, Northern Thailand
(Elsevier, 2020-10) Gunathilake, M. B; Amaratunga, V; Perera, A; Karunanayake, C; Gunathilake, A. S; Rathnayake, U. S
Study region: The Upper Nan River Basin, Northern Thailand Study focus: Precipitation is a major component of the hydrological cycle. A large number of remotely sensed precipitation products are used in hydro-meteorological studies. The accuracy of these relies on basin climatology, basin topography, precipitation mechanism and precipitation sampling techniques used in satellites. Hence, the precipitation products should be validated. Numerous studies have evaluated the reliability of satellite-based precipitation products (SbPPs) in the tropical Asia. However, a handful of research has yet examined the reliability of these in Thailand. Therefore, in this study the reliability of six SbPPs namely, PERSIANN, PER- SIANN
Open Access
Unveiling the Economic Determinants of Child Labour in Africa: A Comprehensive Study of 37 Countries
(Springer Nature, 2025-02-28) Muthugala, H; Magammana, T; Bandara, A; Perera, A; Jayathilaka, R
This study investigates the impact of unemployment, household income and expenditure, globalisation, and foreign direct investment (FDI) on child labour across 37 African countries from 2010 to 2021, employing panel and multiple linear regression models. The findings reveal diverse impacts: rising unemployment significantly increased child labour in countries like Ethiopia and Niger, while in Cameroon and Kenya, it had a negative effect. Globalisation’s influence varied, strongly reducing child labour in Ghana but exacerbating it in Burundi. Household income and expenditure generally reduced child labour, particularly in Ethiopia and Zambia. The effect of FDI was also mixed, decreasing child labour in Madagascar but increasing it in countries with weaker governance. These insights underscore the necessity for tailored, country-specific policies that consider local economic conditions and governance quality. Future efforts to combat child labour must focus on developing sustainable solutions that address these complex dynamics.