Abstract:
Reliable information plays a pivotal role in sustainable urban planning. With
advancements in computer technology, geoinformatics tools enable accurate
identification of land use and land cover (LULC) in both spatial and temporal
dimensions. Given the need for precise information to enhance decision-making,
it is imperative to assess the performance and reliability of classification
algorithms in detecting LULC changes. While research on the application of
machine learning algorithms in LULC evaluation is widespread in many countries,
it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the
reliability and performance of support vector machine (SVM), random forest
(RF), and artificial neural network (ANN) algorithms for detecting changes in land
use and land cover taking Lusaka and Colombo City as the study area from
1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager
(OLI). The results reveal that the RF and ANN models exhibited superior
performance, both achieving Mean Overall Accuracy (MOA) of 96% for
Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM
model yielded Overall Accuracy (OA) ranging between 77% and 94% for the
years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA
and kappa coefficients, ranging between 0.92 and 0.97, when compared to both
the ANN and SVM models, across both study areas. A predominant land use
change was observed as the expansion of vegetation by 11,990 ha (60.4%),
primarily through the conversion of 1,926 ha of bare lands into vegetation in
Lusaka during 1995–2005. However, a noteworthy shift was observed as built-up
areas experienced significant growth from 2005 to 2023, with a total increase of
25,110 ha (71%). However, despite the conversion of vegetation to built-up areas
during the entire period from 1995 to 2023, there was still a net gain of over
11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas(62.3%) during concerned period. LULC simulation also indicated a 160-ha
expansion of built-up areas during the 2023–2035 period in Lusaka. Likewise,
Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the
RF algorithm outperformed the ANN and SVM algorithms. Additionally, the
prediction and simulation results indicate an upward trend in built-up areas in
both scenarios. The resultant land cover maps provide a crucial baseline that will be
invaluable for urban planning and policy development agencies in both countries.
expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha
