Bwalya Mutale, Neel Chaminda Withanage, Prabuddh Kumar Mishra, Jingwei Shen, Kamal Abdelrahman, Mohammed S. Fnais
{"title":"随机森林、人工神经网络和支持向量机学习算法在预测土地利用-土地覆被时空动态方面的性能评估:以卢萨卡和科伦坡为例","authors":"Bwalya Mutale, Neel Chaminda Withanage, Prabuddh Kumar Mishra, Jingwei Shen, Kamal Abdelrahman, Mohammed S. Fnais","doi":"10.3389/fenvs.2024.1431645","DOIUrl":null,"url":null,"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 expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (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.","PeriodicalId":12460,"journal":{"name":"Frontiers in Environmental Science","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo\",\"authors\":\"Bwalya Mutale, Neel Chaminda Withanage, Prabuddh Kumar Mishra, Jingwei Shen, Kamal Abdelrahman, Mohammed S. Fnais\",\"doi\":\"10.3389/fenvs.2024.1431645\",\"DOIUrl\":null,\"url\":null,\"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 expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (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. 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A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo
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 expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (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.
期刊介绍:
Our natural world is experiencing a state of rapid change unprecedented in the presence of humans. The changes affect virtually all physical, chemical and biological systems on Earth. The interaction of these systems leads to tipping points, feedbacks and amplification of effects. In virtually all cases, the causes of environmental change can be traced to human activity through either direct interventions as a consequence of pollution, or through global warming from greenhouse case emissions. Well-formulated and internationally-relevant policies to mitigate the change, or adapt to the consequences, that will ensure our ability to thrive in the coming decades are badly needed. Without proper understanding of the processes involved, and deep understanding of the likely impacts of bad decisions or inaction, the security of food, water and energy is a risk. Left unchecked shortages of these basic commodities will lead to migration, global geopolitical tension and conflict. This represents the major challenge of our time. We are the first generation to appreciate the problem and we will be judged in future by our ability to determine and take the action necessary. Appropriate knowledge of the condition of our natural world, appreciation of the changes occurring, and predictions of how the future will develop are requisite to the definition and implementation of solutions.
Frontiers in Environmental Science publishes research at the cutting edge of knowledge of our natural world and its various intersections with society. It bridges between the identification and measurement of change, comprehension of the processes responsible, and the measures needed to reduce their impact. Its aim is to assist the formulation of policies, by offering sound scientific evidence on environmental science, that will lead to a more inhabitable and sustainable world for the generations to come.