Browsing by Author "Muttil, N"

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Open Access
Analysis of Meandering River Morphodynamics Using Satellite Remote Sensing Data—An Application in the Lower Deduru Oya (River), Sri Lanka
(MDPI, 2022-07-16) Basnayaka, V; Samarasinghe, J. T; Gunathilake, M. B; Muttil, N; Hettiarachchi, D. C; Abeynayaka, A; Rathnayake, U
River meandering and anabranching have become major problems in many large rivers that carry significant amounts of sediment worldwide. The morphodynamics of these rivers are complex due to the temporal variation of flows. However, the availability of remote sensing data and geographic information systems (GISs) provides the opportunity to analyze the morphological changes in river systems both quantitatively and qualitatively. The present study investigated the temporal changes in the river morphology of the Deduru Oya (river) in Sri Lanka, which is a meandering river. The study covered a period of 32 years (1989 to 2021), using Landsat satellite data and the QGIS platform. Cloud-free Landsat 5 and Landsat 8 satellite images were extracted and processed to extract the river mask. The centerline of the river was generated using the extracted river mask, with the support of semi-automated digitizing software (WebPlotDigitizer). Freely available QGIS was used to investigate the temporal variation of river migration. The results of the study demonstrated that, over the past three decades, both the bend curvatures and the river migration rates of the meandering bends have generally increased with time. In addition, it was found that a higher number of meandering bends could be observed in the lower (most downstream) and the middle parts of the selected river segment. The current analysis indicates that the Deduru Oya has undergone considerable changes in its curvature and migration rates.
Open Access
Analysis of Multi-Temporal Shoreline Changes Due to a Harbor Using Remote Sensing Data and GIS Techniques
(MDPI, 2023-05-06) Zoysa, S; Basnayake, V; Samarasinghe, J. T.; Gunathilake, M.B.; Kantamaneni, K; Muttil, N; Muttil, U; Rathnayake, U
Coastal landforms are continuously shaped by natural and human-induced forces, exacerbating the associated coastal hazards and risks. Changes in the shoreline are a critical concern for sustainable coastal zone management. However, a limited amount of research has been carried out on the coastal belt of Sri Lanka. Thus, this study investigates the spatiotemporal evolution of the shoreline dynamics on the Oluvil coastline in the Ampara district in Sri Lanka for a two-decade period from 1991 to 2021, where the economically significant Oluvil Harbor exists by utilizing remote sensing and geographic information system (GIS) techniques. Shorelines for each year were delineated using Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager images. The Normalized Difference Water Index (NDWI) was applied as a spectral value index approach to differentiate land masses from water bodies. Subsequently, the Digital Shoreline Analysis System (DSAS) tool was used to assess shoreline changes, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), End Point Rate (EPR), and Linear Regression Rate (LRR). The results reveal that the Oluvil coast has undergone both accretion and erosion over the years, primarily due to harbor construction. The highest SCE values were calculated within the Oluvil harbor region, reaching 523.8 m. The highest NSM ranges were recorded as −317.1 to −81.3 m in the Oluvil area and 156.3–317.5 m in the harbor and its closest point in the southern direction. The maximum rate of EPR was observed to range from 3 m/year to 10.7 m/year towards the south of the harbor, and from −10.7 m/year to −3.0 m/year towards the north of the harbor. The results of the LRR analysis revealed that the rates of erosion anomaly range from −3 m/year to −10 m/year towards the north of the harbor, while the beach advances at a rate of 3 m/year to 14.3 m/year towards the south of the harbor. The study area has undergone erosion of 40 ha and accretion of 84.44 ha. These findings can serve as valuable input data for sustainable coastal zone management along the Oluvil coast in Sri Lanka, safeguarding the coastal habitats by mitigating further anthropogenic vulnerabilities.
Open Access
The Assessment of Climate Change Impacts and Land-use Changes on Flood Characteristics: The Case Study of the Kelani River Basin, Sri Lanka
(MDPI, 2022-10-09) Samarasinghe, J. T; Makumbura, R. K; Wickramarachchi, C; Sirisena, J; Gunathilake, M.B; Muttil, N; Yenn Teo, F; Rathnayake, U
Understanding the changes in climate and land use/land cover (LULC) over time is important for developing policies for minimizing the socio-economic impacts of riverine floods. The present study evaluates the influence of hydro-climatic factors and anthropogenic practices related to LULC on floods in the Kelani River Basin (KRB) in Sri Lanka. The gauge-based daily precipitation, monthly mean temperature, daily discharges, and water levels at sub-basin/basin outlets, and both surveyed and remotely sensed inundation areas were used for this analysis. Flood characteristics in terms of mean, maximum, and number of peaks were estimated by applying the peak over threshold (POT) method. Nonparametric tests were also used to identify the climatic trends. In addition, LULC maps were generated over the years 1988–2017 using Landsat images. It is observed that the flood intensities and frequencies in the KRB have increased over the years. However, Deraniyagala and Norwood sub-basins have converted to dry due to the decrease in precipitation, whereas Kithulgala, Holombuwa, Glencourse, and Hanwella showed an increase in precipitation. A significant variation in atmospheric temperature was not observed. Furthermore, the LULC has mostly changed from vegetation/barren land to built-up in many parts of the basin. Simple correlation and partial correlation analysis showed that flood frequency and inundation areas have a significant correlation with LULC and hydro-climatic factors, especially precipitation over time. The results of this research will therefore be useful for policy makers and environmental specialists to understand the relationship of flood frequencies with the anthropogenic influences on LULC and climatic factors.
Open Access
Comparison of Calibration Approaches of the Soil and Water Assessment Tool (SWAT) Model in a Tropical Watershed
(MDPI, 2022-10-22) Makumbura, R. K; Gunathilake, M. B; Samarasinghe, J. T; Confesor, R; Muttil, N; Rathnayak, U
Hydrologic models are indispensable tools for water resource planning and management. Accurate model predictions are critical for better water resource development and management decisions. Single-site model calibration and calibrating a watershed model at the watershed outlet are commonly adopted strategies. In the present study, for the first time, a multi-site calibration for the Soil and Water Assessment Tool (SWAT) in the Kelani River Basin with a catchment area of about 2340 km2 was carried out. The SWAT model was calibrated at five streamflow gauging stations, Deraniyagala, Kithulgala, Holombuwa, Glencourse, and Hanwella, with drainage areas of 183, 383, 155, 1463, and 1782 km2 , respectively, using three distinct calibration strategies. These strategies were, utilizing (1) data from downstream and (2) data from upstream, both categorized here as single-site calibration, and (3) data from downstream and upstream (multi-site calibration). Considering the performance of the model during the calibration period, which was examined using the statistical indices R 2 and NSE, the model performance at Holombuwa was upgraded from “good” to “very good” with the multi-site calibration technique. Simultaneously, the PBIAS at Hanwella and Kithulgala improved from “unsatisfactory” to “satisfactory” and “satisfactory” to “good” model performance, while the RSR improved from “good” to “very good” model performance at Deraniyagala, indicating the innovative multi-site calibration approach demonstrated a significant improvement in the results. Hence, this study will provide valuable insights for hydrological modelers to determine the most appropriate calibration strategy for their large-scale watersheds, considering the spatial variation of the watershed characteristics, thereby reducing the uncertainty in hydrologic predictions.
Open Access
Evaluation of the Impact of Land Use Changes on Soil Erosion in the Tropical Maha Oya River Basin, Sri Lanka
(MDPI, 2023-01) Palliyaguru, C; Basnayake, V; Makumbura, R. K; Gunathilake, M. B; Muttil, N; Wimalasiri, E. M; Rathnayake, U
Soil degradation is a serious environmental issue in many regions of the world, and Sri Lanka is not an exception. Maha Oya River Basin (MORB) is one of the major river basins in tropical Sri Lanka, which suffers from regular soil erosion and degradation. The current study was designed to estimate the soil erosion associated with land use changes of the MORB. The Revised Universal Soil Loss Equation (RUSLE) was used in calculating the annual soil erosion rates, while the Geographic Information System (GIS) was used in mapping the spatial variations of the soil erosion hazard over a 30-year period. Thereafter, soil erosion hotspots in the MORB were also identified. The results of this study revealed that the mean average soil loss from the MORB has substantially increased from 2.81 t ha−1 yr−1 in 1989 to 3.21 t ha−1 yr−1 in 2021, which is an increment of about 14.23%. An extremely critical soil erosion-prone locations (average annual soil loss > 60 t ha−1 yr−1) map of the MORB was developed for the year 2021. The severity classes revealed that approximately 4.61% and 6.11% of the study area were in high to extremely high erosion hazard classes in 1989 and 2021, respectively. Based on the results, it was found that the extreme soil erosion occurs when forests and vegetation land are converted into agricultural and bare land/farmland. The spatial analysis further reveals that erosion-prone soil types, steep slope areas, and reduced forest/vegetation cover in hilly mountain areas contributed to the high soil erosion risk (16.56 to 91.01 t ha−1 yr−1) of the MORB. These high soil erosional areas should be prioritized according to the severity classes, and appropriate land use/land cover (LU/LC) management and water conservation practices should be implemented as recommended by this study to restore degraded lands.
Open Access
A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India
(MDPI, 2022-11-20) Pawar, U; Suppawimut, W; Muttil, N; Rathnayake, U
The Upper Krishna Basin in Maharashtra (India) is highly vulnerable to floods. This study aimed to generate a flood susceptibility map for the basin using Frequency Ratio and Statistical Index models of flood analysis. The flood hazard inventory map was created by 370 flood locations in the Upper Krishna Basin and plotted using ArcGIS 10.1 software. The 259 flood locations (70%) were selected randomly as training samples for analysis of the flood models, and for validation purposes, the remaining 111 flood locations (30%) were used. Flood susceptibility analyses were performed based on 12 flood conditioning factors. These were elevation, slope, aspect, curvature, Topographic Wetness Index, Stream Power Index, rainfall, distance from the river, stream density, soil types, land use, and distance from the road. The Statistical Index model revealed that 38% of the area of the Upper Krishna Basin is in the high- to very-high-flood-susceptibility class. The precision of the flood susceptibility map was confirmed using the receiver operating characteristic and the area under the curve value method. The area under the curve showed a 66.89% success rate and a 68% prediction rate for the Frequency Ratio model. However, the Statistical Index model provided an 82.85% success rate and 83.23% prediction rate. The comparative analysis of the Frequency Ratio and Statistical Index models revealed that the Statistical Index model was the most suitable for flood susceptibility analysis and mapping flood-prone areas in the Upper Krishna Basin. The results obtained from this research can be helpful in flood disaster mitigation and hazard preparedness in the Upper Krishna Basin.
Open Access
Modelling Potential Soil Erosion and Sediment Delivery Risk in Plantations of Sri Lanka
(MDPI, 2022-12-14) Karunaratne, A. S; Wimalasiri, E. M; Piyathilake, U; Gunatilake, S. K; Muttil, N; Rathnayake, U
The current trend in agricultural practices is expected to have a detrimental impact in terms of accelerating soil erosion. Assessment of the cumulative impact of various management strategies in a major plantation is a measure of the sustainably of soil resources. Thus, the current study aimed to develop the potential soil erosion map for a selected plantation (8734 ha in size) in tropical Sri Lanka using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. The estimated mean annual soil loss rate of the selected plantation was 124.2 t ha−1 ranging from 0.1 to 6903.3 t ha−1. Out of the total extent, ~49.5% of the area belongs to the low soil erosion hazard category (0–5 t ha−1 year−1) while ~7.8% falls into very high (25–60 t ha−1 year−1) and ~1.3% into extremely high (60 < t ha−1 year−1) soil erosion hazard classes. The rainfall erosivity factor (R) for the entire study area is 364.5 ± 98.3 MJ mm ha−1 hr−1. Moreover, a relatively higher correlation was recorded between total soil loss and R factor (0.3) followed by C factor (0.2), P factor (0.2), LS factor (0.1), and K factor (<0.1). It is evident that rainfall plays a significant role in soil erosion in the study area. The findings of this study would help in formulating soil conservation measures in the plantation sector in Sri Lanka, which will contribute to the country’s meeting of the UN Sustainable Development Goals (SDGs).
Open Access
Pavement Roughness Prediction Using Explainable and Supervised Machine Learning Technique for Long-Term Performance
(MDPI, 2023-06-15) Sandamal, K; Shashiprabha, S; Muttil, N; Rathnayake, U
Maintaining and rehabilitating pavement in a timely manner is essential for preserving or improving its condition, with roughness being a critical factor. Accurate prediction of road roughness is a vital component of sustainable transportation because it helps transportation planners to develop cost-effective and sustainable pavement maintenance and rehabilitation strategies. Traditional statistical methods can be less effective for this purpose due to their inherent assumptions, rendering them inaccurate. Therefore, this study employed explainable and supervised machine learning algorithms to predict the International Roughness Index (IRI) of asphalt concrete pavement in Sri Lankan arterial roads from 2013 to 2018. Two predictor variables, pavement age and cumulative traffic volume, were used in this study. Five machine learning models, namely Random Forest (RF), Decision Tree (DT), XGBoost (XGB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were utilized and compared with the statistical model. The study findings revealed that the machine learning algorithms’ predictions were superior to those of the regression model, with a coefficient of determination (R2) of more than 0.75, except for SVM. Moreover, RF provided the best prediction among the five machine learning algorithms due to its extrapolation and global optimization capabilities. Further, SHapley Additive exPlanations (SHAP) analysis showed that both explanatory variables had positive impacts on IRI progression, with pavement age having the most significant effect. Providing accurate explanations for the decision-making processes in black box models using SHAP analysis increases the trust of road users and domain experts in the predictions generated by machine learning models. Furthermore, this study demonstrates that the use of explainable AI-based methods was more effective than traditional regression analysis in IRI prediction. Overall, using this approach, road authorities can plan for timely maintenance to avoid costly and extensive rehabilitation. Therefore, sustainable transportation can be promoted by extending pavement life and reducing frequent reconstruction.
Open Access
Planform Changes in the Lower Mahaweli River, Sri Lanka Using Landsat Satellite Data
(MDPI, 2022-10-03) Basnayaka, V; Samarasinghe, J. T; Gunathilake, M. B; Muttil, N; Rathnayake, U
Major development projects along rivers, like reservoirs and other hydraulic structures, have changed not only river discharges but also sediment transport. Thus, changes in river planforms can be observed in such rivers. In addition, river centerline migrations can be witnessed. The Mahaweli River is the longest in Sri Lanka, having the largest catchment area among the 103 major river basins in the country. The river has been subjected to many development projects over the last 50 years, causing significant changes in the river discharge and sediment transport. However, no research has been carried out to evaluate the temporal and spatial changes in planforms. The current seeks to qualitatively analyze the river planform changes of the Lower Mahaweli River (downstream to Damanewewa) over the past 30 years (from 1991 to 2021) and identify the major planform features and their spatiotemporal changes in the lower Mahaweli River. Analyzing the changes in rivers requires long-term data with high spatial resolution. Therefore, in this research, remotely sensed Landsat satellite data were used to analyze the planform changes of Lower Mahaweli River with a considerably high resolution (30 m). These Landsat satellite images were processed and analyzed using the QGIS mapping tool and a semi-automated digitizing tool. The results show that major changes in river Mahaweli occurred mainly in the most downstream sections of the selected river segment. Further, the river curvature was also comparatively high downstream of the river. An oxbow lake formation was observed over time in the most downstream part of the Mahaweli River after 2011. Centerline migration rates were also calculated with the generated river centerlines. It was found that the rates were generally lower than about 30 m per year, except for at locations where river meandering was observed. The main limitations of this study were the possible misclassifications due to the resolution of images and obstructions caused by cloud cover in the Landsat images. To achieve more accurate estimates, this study could be developed further with quantitative mathematical analysis by also considering the sediment dynamics of the Mahaweli River.
Open Access
Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence
(MDPI, 2022-06-10) Meddage, D. P. P; Ekanayake, I. U; Herath, S; Gobirahavan, R; Muttil, N; Rathnayake, U
Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions
Open Access
Reviving Urban Landscapes: Harnessing Pervious Concrete Pavements with Recycled Materials for Sustainable Stormwater Management
(Multidisciplinary Digital Publishing Institute (MDPI), 2025-10-29) Gunathilake, T.A; Siriwardhana,K.D; Miguntanna,N; Miguntanna, Nadeeka; Rathnayake, U; Muttil, N
This study examines the effectiveness of pervious concrete pavements as a sustainable and cost-effective stormwater management technique, particularly by incorporating locally sourced recycled materials into their design. It evaluates the stormwater treatment potential of three pervious concrete pavement types incorporating recycled plastic, glass, and crushed concrete aggregates, with six design variations produced using 25% and 50% replacements of coarse aggregates from these materials. The key properties of pervious concrete, namely compressive strength, porosity, unit weight, and infiltration, and key water quality indicators, namely pH, electrical conductivity (EC), total suspended solids (TSS), colour, turbidity, chemical oxygen demand (COD), nitrate (NO3−), and orthophosphate (PO43−), were analysed. Results indicated an overall improvement in the quality of the stormwater runoff passed through all pervious concrete pavements irrespective of composition. Notable reductions in turbidity, TSS, colour, COD, PO43−, and NO3− underscored the effectiveness of pervious concrete containing waste materials in the treatment of stormwater runoff. Pervious concrete pavements with 25% recycled concrete exhibited optimal performance in reducing TSS, COD, and PO43− levels, while the 50% recycled concrete variant excelled in diminishing turbidity. However, the study found that the use of recycled materials in pervious concrete pavements affects properties like compressive strength and infiltration rate differently. While incorporating 25% and 50% recycled concrete aggregates did not significantly reduce compressive strength, the effectiveness of stormwater treatment varied based on the mix design and type of recycled material used. Thus, this study highlights the potential of utilizing recycled waste materials in pervious concrete pavements for sustainable stormwater management.