A machine learning approach to monitoring and forecasting spatio-temporal dynamics of land cover in Cox's Bazar district, Bangladesh from 2001 to 2019

Roy, Bishal

doi:10.1016/j.envc.2021.100237

Cited by 18 publications

(8 citation statements)

References 36 publications

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“…RFR is a nonlinear model that is suitable for the regression of datasets that do not exhibit linearity. RFR can capture the existing and potential interactions between the type and intensity of an LULC class and the influencing (driving) factors [81]. In its implementation, the proposed RFR-LULC change simulation and prediction model first extracts the multitemporal class area changes, from which the transition probability matrices for the successive years are derived.…”

Section: Random Forest Regression For Lulc Predictionmentioning

confidence: 99%

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Ouma,

Nkwae,

Odirile

et al. 2024

Sustainability

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For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.

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Section: Random Forest Regression For Lulc Predictionmentioning

confidence: 99%

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Ouma,

Nkwae,

Odirile

et al. 2024

Sustainability

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“…First of all, an ANN has been used in order to calculate the LULC pattern in the study area. Previous studies have followed different traditional and machine learning algorithms to measure and predict the LULC pattern of Cox's Bazar [18], [42]- [44]. ANN is found to be more accurate in terms of providing results compared to other types of algorithms for measuring LULC [42], [45]- [48].…”

Section: Impact Of Rohingya Refugee Influx On Lstmentioning

confidence: 99%

Artificial Neural Network-Based Land Use-Specific Carbon Patterns and Their Effects on Land Surface Temperature as a Result of the Rohingya Refugee Influx

Sarkar,

Saroar,

Das

et al. 2023

IEEE Access

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The objective of the research is to investigate how refugees' influx has altered the carbon dynamics of different land uses and the relationship between land use specific carbon emissions and land surface temperature (LST). Two upazilais of the Cox's Bazar district, Bangladesh (i.e., Ukhiya and Teknaf), were mostly affected by the Rohingya refugee influx and are the focus of the study. The study classified the land use land cover (LULC) into four classes (e.g., agricultural, forest, settlement, and water) for two different time periods (i.e., before and after the influx of Rohingya refugees) using an artificial neural network algorithm and sentinel satellite imagery. Carbon emissions and absorptions specific to land use were calculated using classified land use land cover and coefficients. Again, two time series of Landsat 8 imagery were applied to estimate land surface temperature shifts. The area of forests was found to have decreased by 21.19 square miles (9.58 percent) and the area of settlements to have increased by 18.24 square miles (8.25 percent) between 2017 and 2021. There was a negative net land-use based carbon emission of -5187.02 tons per year in 2017. In 2021, it was predicted that annual net emissions would total 2208.24 tons. LST during the study period has increased as a result of human activities that release greenhouse gases into the atmosphere. The findings of this research will inform policymakers' decisions about the conservation and sustainable development of natural resources in the region experiencing an influx of Rohingya refugees.

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“…With the rapid development of artificial intelligence in recent years, machine learning has emerged as a prominent research tool in land planning (Sankarrao et al, 2021;Dong et al, 2022). Commonly used machine learning algorithms in land planning include random forest (RF) (Roy, 2021), support vector machine (SVM) (Marjanović et al, 2011), BP neural network (Hong et al, 2016;Aishwarya Devendran and Lakshmanan, 2017), gradient boosting decision tree (GBDT) (Xia and Zhai, 2022), Quadtree Algorithm (Xia et al, 2023), XGBoost (Huang et al, 2023) and various hybrid models (Gaur et al, 2020;Gharaibeh et al, 2020;Chaturvedi and de Vries, 2021). However, the application of machine learning in ecological-agricultural-urban space evaluation is still limited.…”

Section: Introductionmentioning

confidence: 99%

Spatial suitability evaluation based on multisource data and random forest algorithm: a case study of Yulin, China

Li,

Zhang,

Hong

et al. 2024

Front. Environ. Sci.

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With a large population and rapid urbanization, there are still many challenges to optimize the ecological-agricultural-urban space. Here, taking Yulin City, situated on the Loess Plateau of China as a case in point, we explored the spatial suitability evaluation of ecological-agricultural-urban space. Building upon the Chinese government’s concept of “resource and environmental carrying capacity and territorial development suitability evaluation” (hereinafter referred to as “double evaluation”), this study applies machine learning to the planning of ecological-agricultural-urban space. It explores an intelligent evaluation method for land space patterns using multi-source data. Based on the random forest (RF) algorithm and geographic information system (GIS), resulting in evaluated spatial patterns for ecological-agricultural-urban in the Yulin area. The results showed the constructed random forest models achieved an accuracy of 93% for ecology, 90% for agriculture, and 92% for urban space in Yulin City on the test dataset. By means of suitability analysis, the results indicated that the extremely important ecological space were predominantly located in the southwestern and eastern regions of the study area, while suitable space for agricultural production were primarily scattered throughout the southeast. In contrast, suitable space for urban construction were concentrated mainly in the central part of the study area. The use of machine learning has proven to be effective in addressing multicollinearity among spatial evaluation factors across three different areas. By eliminating human subjectivity in weight assignment during evaluation, it introduces fresh perspectives for land space planning and status assessment. These findings may offer support for the scientific delineation of ecological-agricultural-urban space (three districts and three lines) in China.

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A machine learning approach to monitoring and forecasting spatio-temporal dynamics of land cover in Cox's Bazar district, Bangladesh from 2001 to 2019

Cited by 18 publications

References 36 publications

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

Artificial Neural Network-Based Land Use-Specific Carbon Patterns and Their Effects on Land Surface Temperature as a Result of the Rohingya Refugee Influx

Spatial suitability evaluation based on multisource data and random forest algorithm: a case study of Yulin, China

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