Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Database
2.3. Descriptions of Models
2.3.1. Multivariate Adaptive Regression Splines (MARS)
2.3.2. Lasso Linear Regression (LLR)
2.3.3. Random Forest Regression (RFR)
2.3.4. Performance Metrics
3. Results Analysis
4. Discussion
- Da Nang city, the most developed urban area in the region, has industrial parks equivalent to the urban land-use area. As a result, it is critical to relocate industrial zones in the ancient city, rationalize land use functions, employ high-tech equipment, and create a green environment. More importantly, to accommodate the influx of migrants from all over the country into the city’s working streets, local authorities must plan to build land funding and infrastructure in industrial zones or rural areas near industrial zones, lowering stress in Da Nang’s central city.
- The Quang Nam and Thua Thien Hue provinces, two provinces with many tangible cultural heritage sites such as Hue City, Hoi An Ancient Town, and My Son Holyland, need to build satellite urban areas to relieve the pressure on infrastructure and population for urban heritage areas. In addition, because the land fund for rural use is enormous, a strategy for converting agricultural land to industrial and commercial zones in rural areas is required to support rural growth and urban areas while also creating jobs for rural residents.
- Quang Binh and Quang Tri are two provinces with slower urban and industrial zone development than the Da Nang, Quang Nam, and Thua Thien-Hue provinces; however, plenty of rural land use and industrial land use funds are being used in these two provinces. As a result, these two provinces will need to construct satellite cities based on highly populated areas near industrial parks. In addition, it is necessary to form sub-regional centers in the district in the direction of commodity production with high technology.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Criteria/Indicators | Type I | Type II | Type III | Type IV | Type V |
---|---|---|---|---|---|
Population | (a) >1 million: Central government-run city (b) 500,000: Provincial city | (c) 300,000 to 1 million: If class 2 is central government-run city, poulation should be more than 800,000 | (d) 100,000 to 350,000 | (e) 500,000 to 350,000 | (f) >4000 |
Nonagricultural labor | 85% | 80% | 70% | 70% | >65% |
Popolation density | (a) 12,000/km2 (b) 10,000/km2 | 8000 /km2 or 10,000 /km2 if the city is directly uncer central government control | 6000 km2 | 4000 km2 | 2000 km2 |
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Province | Rural Land-Use (ha) | Industrial Land-Use (ha) | Urban Land-Use (ha) | Sub-Total |
---|---|---|---|---|
Quang Binh | 5632 | 3103 | 1238 | 9973 |
Quang Tri | 3067 | 1740 | 1534 | 6341 |
Thua Thien-Hue | 6420 | 4596 | 3494 | 14,510 |
Da Nang | 2464 | 4694 | 4676 | 11,834 |
Quang Nam | 17,024 | 6751 | 4634 | 28,409 |
Total | 34,607 | 20,884 | 15,576 | 71,067 |
Province | St Dev (ha) | Mean (ha) | Min (ha) | Max (ha) | Skewness | Kurtosis |
---|---|---|---|---|---|---|
Quang Binh | 214 | 878 | 608 | 1238 | 0.17 | −1.17 |
Quang Tri | 84 | 1369 | 1262 | 1534 | 0.64 | −0.86 |
Thua Thien-Hue | 959 | 4076 | 3272 | 5434 | 0.58 | −1.72 |
Da Nang | 403 | 4317 | 3514 | 4676 | 1.03 | −0.67 |
Quang Nam | 183 | 4219 | 4093 | 4634 | 0.87 | 1.65 |
Quang Binh | Quang Tri | Thua Thien-Hue | Da Nang | Quang Nam | Average | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
LLR | RFR | MARS | LLR | RFR | MARS | LLR | RFR | MARS | LLR | RFR | MARS | LLR | RFR | MARS | LLR | RFR | MARS | |
MSE | 143 | 10 | 5 | 107 | 10 | 4 | 535 | 151 | 162 | 98 | 58 | 84 | 58 | 10 | 8 | 188.2 | 47.8 | 52.6 |
MAE | 27 | 4 | 5 | 33 | 6 | 6 | 442 | 70 | 162 | 78 | 79 | 75 | 39 | 8 | 6 | 123.8 | 33.4 | 50.8 |
RMSE | 38 | 11 | 6 | 39 | 9 | 9 | 535 | 150 | 131 | 98 | 98 | 84 | 59 | 10 | 8 | 153.8 | 55.6 | 47.6 |
R | 0.91 | 0.91 | 0.91 | 0.82 | 0.91 | 0.91 | 0.67 | 0.92 | 0.91 | 0.89 | 0.87 | 0.89 | 0.86 | 0.91 | 0.92 | 0.83 | 0.904 | 0.908 |
R2 | 0.92 | 0.94 | 0.94 | 0.66 | 0.93 | 0.94 | 0.56 | 0.92 | 0.92 | 0.9 | 0.9 | 0.91 | 0.84 | 0.94 | 0.94 | 0.776 | 0.926 | 0.93 |
GCV | 88 | 193 | 66,746 | 7522 | 77 |
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Giang, N.H.; Wang, Y.-R.; Hieu, T.D.; Ngu, N.H.; Dang, T.-T. Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam. Sustainability 2022, 14, 5194. https://doi.org/10.3390/su14095194
Giang NH, Wang Y-R, Hieu TD, Ngu NH, Dang T-T. Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam. Sustainability. 2022; 14(9):5194. https://doi.org/10.3390/su14095194
Chicago/Turabian StyleGiang, Nguyen Hong, Yu-Ren Wang, Tran Dinh Hieu, Nguyen Huu Ngu, and Thanh-Tuan Dang. 2022. "Estimating Land-Use Change Using Machine Learning: A Case Study on Five Central Coastal Provinces of Vietnam" Sustainability 14, no. 9: 5194. https://doi.org/10.3390/su14095194