Abstract
Vietnam’s economy with agriculture and aquaculture still account for roughly 26% of the country’s gross domestic product, and nearly 70% of the Vietnamese population lives in rural areas; therefore, agriculture and aquaculture land use play a crucial role in the development process of Vietnam. Rapidly increasing population and infrastructure in rural areas and industrial zones lead to these land-use changes. Hence, these land-use change predictions are crucial for local authorities and the local people to make land-resource funds and set up planning. This article suggests support vector regression (SVR), artificial neural network (ANN), and seasonal autoregressive integrated moving average (SARIMA) methods to predict land-use change. By comparing the three models, the results indicate that almost all of the SVR models improve the accurate performance more than ANN and SARIMA in Quangtri, ThuThienHue, Danang, and Quảngnam provinces. Furthermore, the ANN model indicates more accurate forecasting than the SVR and SARIMA models in Quan Binh province. The result may be support for the Ministry of Natural Resources and Environment to conduct the land-use inventory and upgrade agriculture and aquaculture land-use change maps every 5 years. Afterward, the Department of Natural Resources and Environment’s provinces use the estimating database and update it manually.
1 Introduction
Spatial distributions of each land use, associated food security, environmental circumstances, socioeconomic conditions, and institutional and cultural issues may all significantly impact the evolution of regional land-use patterns [1,2,3,4,5,6]. Hence, several land, atmospheric, and environmental science applications are interested in the spatial distribution of land use as agriculture, aquaculture, forest, and urban [7,8]. Urbanization in Vietnam has extended residential and industrial zones into previously open, low-populated areas that were originally natural places for aquaculture and agricultural land uses during the last three decades [9,10]. On the other hand, agriculture and aquaculture still account for roughly 26% of the gross domestic product, and nearly 70% of the Vietnamese population lives in rural areas. As a result, land plays a critical role in Vietnam’s growth [11,12].
Land-use change prediction methodologies include three main groups: Geographic Information System (GIS), statistical, and artificial intelligence (AI) methods [13,14,15,16,17,18,19,20,21,22]. Apart from the GIS method, the boundary between the statistical and artificial neural network (ANN) methods is quite complicated. The literature uses several techniques that consist of a statistical technique, namely, seasonal autoregressive integrated moving average (SARIMA), or ARIMA models [23,24], and two AI techniques, namely, ANN and support vector machines [25,26,27,28,29,30]. Recently, many studies have applied machine learning to analyze land-use changes, as shown below. Wang et al. [28] used various kernels to forecast the correlations between cultivated land area and impact land-use change factors, including radial basis function, polynomial, and sigmoid kernels. The result indicated that it could be best used to estimate the cultivated land areas. Jiang et al. [31] used three models, named SARIMA, dynamic harmonics regression (DHR), and seasonal-trend decomposition procedure based on loss (STL) to simulate the moderate resolution imaging spectroradiometer leaf area index (LAI) from 2001 to 2006 and predict short-term LAI values in the future (2007). The study result showed that the SARIMA model was better at estimating R 2 values in all land cover types than the DHR or STL models. Li et al. [32] employed three different models of the previous 5 years’ weekly average, seasonal decomposition (SD) and Box–Jenkins multiplicative SARIMA models to predict the annual cycle of the weekly soil dryness index (SDI) series in the southwest of Western Australia. The result showed that the SARIMA model outstripped the other models in estimating the annual cycle of SDI data. Kong et al. [33] used GIS and ANN methodologies to assess geo-environmental suitability for agricultural land use in Hangzhou, China’s rural–urban periphery. The result showed the two models successfully criticized agricultural land-use suitability based on the geomorphology, soil conditions, basic infrastructure, and geological environment. Zhang and Chen [34] evaluated the suitability of urban land use in Hefei using the ANN model. The research suggests that the model might be used to assess the feasibility of urban land use. Hence, these support vector regression (SVR), ANN, and SARIMA models are suitably deployed for land-use change prediction in Vietnam’s five central coastal provinces (Quangbinh, Quangtri, ThuaThienHue, Danang, and Quang Nam).
The study aims to describe the forecast of agriculture and aquaculture land-use change using SVR, ANN, and SARIMA algorithms. The models’ input vectors are based on 44 quarters of land usage across 11 years in five central coastal provinces in Vietnam (Quangbinh, Quangtri, ThuaThienHue, Danang, and Quang Nam). This study also highlights a comparison among SVR, ANN, and SARIMA that is based on the results of statistical accuracy parameters such as mean (M), root mean square error (RMSE), mean absolute error (MAE), standard deviation (St Dev), Pearson correlation coefficient (R), kurtosis coefficient (Kurt), skewness coefficient (Skew), minimum (Min), maximum (Max), and correlation of determination (R 2). The results of these three models may show the working efficiency of the models for land-use prediction. In addition, by projecting these types of land-use changes, future scenarios that can aid land-use planning and decision-making can be developed.
The following is the content of the article. The first section of the article is an introduction. The SVR, ANN, and SARIMA models are introduced in Section 2 and explained in detail for use throughout this study. The study findings and discussions are explained in Sections 3 and 4. Finally, the conclusions are presented in Section 5.
2 Study area and methodology
2.1 Study area
Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam provinces are located in the central coastal area of Vietnam (see Figure 1). This area’s topography has a lower elevation, starting from the mountainous range down to the midland hills, to the plains inside the coastal dunes, and finally to the coastal islands. The delta area has a coastal-mountainous nature, and it is divided by mountain branches close to the sea as Hoanh Son mountain range – Ngang pass, Bach Ma mountain range – Hai Van pass [35]. These provinces’ total land area is 2,950,669 ha, in which, Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam occupy 799,876, 470,123, 494,711, 128,473, and 1,057,486 ha, respectively.
Five provinces’ locations.
2.2 ANN model
An ANN model proposed in this study was created using Keras Neural Network, a highly modular neural network library. TensorFlow or Theano may be chosen as the backend [36]. Hence, Keras also contains plentiful modules that include activation function, layer, preprocessing, objective function, optimization method selection, etc. Deploying these modules indicates that network models may be easily set up, and core parameters of neural networks may be better enhanced [37]. The structure of the Keras Neural Network consists of a three-layer neuronal architecture as an input layer, hidden layer, and output layer [38]. The hidden layer may contain one or multilayers. The backpropagation learning algorithm of Levenberg–Marquardt has been used for the data feature extraction contained in the model’s inputs [39,40]. Figure 2 shows a structural view of the ANN model, as the below-input layer has m input nodes from i
1 to i
m
, one neuron of the output layer. There are n hidden layers as the first hidden layer contains neurons from H
11 to H
1m
, the second one is from H
21 to H
2m
, and the last one is from H
n1 to H
nm
. Each neuron has a resemble Weight and Bias in the hidden layer, and the output layer, such as
The architecture of ANN for land-use prediction.
2.3 SVR model
Regarding a set of training data {(x 1 , y 1), (x 2 , y 2)… (x n , y n )}, where x i, y i, and n are an input, a target output, and a number of samples, respectively. Assuming SVR is to demonstrate a function of f(x) that may estimate accurate future values [43], and it is supposed by:
where K is the kernel function (named linear, sigmoid, radial basis, and polynomial functions), which nonlinearly maps the input data into higher feature space, c is the bias, α* and α are the Lagrange multipliers that are optimized by a maximum of the quadratic function as follows:
where C is called the penalty indicator, which commands the sanctioned degree on samples whose errors are beyond ε. Normally, only certain parts of the samples may suit the property:
The kernel-based SVR is a powerful tool for solving regression problems. First, the kernel is chosen in a data-dependent way. Then, the applied kernel is a commonly linear function. They then describe different mappings from lower space to higher feature space [44,45].
2.4 SARIMA model
If the time series X t is considered as the SARIMA (p, d, q) × (P, D, Q, s) model with (p, d, q) nonseasonal order terms, (P, D, Q) seasonal order terms have the structure as follows [46,47,48]:
where p and seasonal P denote several autoregressive terms (lags of the stationary series); d and seasonal D denote differencing that must be done to stationary series, q and seasonal Q demonstrate a number of moving average terms (lags of the forecast errors), s demonstrates the seasonal length in the data, ω t and B denote the backward shift operator and the white noise value at time t.
Equation (3) may be rewritten mathematically after replacing the value of
The SARIMA components may be rewritten as follows:
and
The d and D point out the order of the nonseasonal and seasonal differencing, and their values are usually less than 1 and 2 totals of seasonal difference, respectively (i.e., 0
2.5 Performance metrics
Predicting outputs rely most on estimating and comparing the actual values to the predicted values. These indicators of the accuracy measurement parameters include the MAE, RMSE, R 2, and R. In addition, the error metrics are defined as follows [53,54,55]:
where
The diagram describes the methodology of this study in Figure 3. First, the collected data on agriculture and aquaculture land uses are preprocessed and checked by statistical methods in the input layer. Next, the data is separated into first quarter to thirty-second quarter for the training phase, and from thirty-third quarter to forty-fourth quarter for the testing phase; simultaneously, the SVR, ANN, and SARIMA algorithms are applied to learn the training samples and choose the optimal network parameters in the process of the methods. Last, the three models’ implementations showed the basis functions and were compared using the accuracy measurement parameters, namely RMSE, MAE, R, and R 2, in the possible result stage and looking for the most fitted estimation model for the study in the output layer.
Diagram of the research steps used in this study.
3 Results
3.1 Database collection
This study uses a database containing land-use change in 44 quarters from 2010 to 2020. Two input variables, agriculture (Agri) land use and aquaculture (Aqua) land use, were gathered from five provinces at the Department of Natural Resources and Environment (DONRE). The typical statistical outcomes are also highlighted in Table 1. The Mean, Min, Max, St Dev, Skew, and Kurt values were calculated from the recorded quarters per year. Five provinces’ input data patterns are chosen in two stages. The first section is utilized for the training phase, which lasted from the first quarter to the thirty-second quarter. The testing phase, which lasted from the thirty-third quarter to the forty-fourth quarter, is the second section. Moreover, this division supports the models to optimize the accuracy parameters, and the univariate time series data forecasting fits the SARIMA model.
Descriptive statistics of land use in five provinces
| Parameter | Quangbinh | Quangtri | ThuaThienHue | Danang | Quảngnam | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Agri | Aqua | Agri | Aqua | Agri | Aqua | Agri | Aqua | Agri | Aqua | |
| Kurtosis | −0.73 | −0.51 | −0.62 | −0.22 | −0.61 | −0.87 | 0.85 | 1.19 | −0.42 | −0.52 |
| Skewness | −1.23 | −1.51 | −1.64 | −1.50 | −1.66 | 0.07 | −0.88 | 0.01 | −1.71 | −1.63 |
| St Dev | 4,631 | 263 | 14,931 | 150 | 4,418 | 81 | 605 | 33 | 46,793 | 102 |
| Mean (ha) | 87,133 | 3,157 | 109,946 | 2,851 | 65,561 | 6,012 | 7,222 | 147 | 173,836 | 3,582 |
| Min (ha) | 79,222 | 2,763 | 88,282 | 2,651 | 58,815 | 5,835 | 6,641 | 117 | 113,495 | 3,431 |
| Max (ha) | 91,858 | 3,468 | 122,583 | 3,058 | 69,258 | 6,133 | 8,295 | 211 | 220,104 | 3,692 |
3.2 Experimental results
Find the best ANN, SVR, and SARIMA models for the land use of five provinces after many tests. With the values of the fundamental parameters shown in Table 2, this investigation discovered the best model.
Basic configuration of ANN, SVR, SARIMA models
| Model | Item | Quangbinh | Quangtri | ThuaThienHue | Danang | Quảngnam | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Agri config | Aqua config | Agri config | Aqua config | Agri config | Aqua config | Agri config | Aqua config | Agri config | Aqua config | ||
| ANN | Model type | Seq | Seq | Seq | Seq | Seq | Seq | Seq | Seq | Seq | Seq |
| Layer type | Dense | Dense | Dense | Dense | Dense | Dense | Dense | Dense | Dense | Dense | |
| Number of inputs | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 | 32 | |
| Number of hidden | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | |
| Hidden layer size | 200/200 | 100/100 | 200/200 | 100/100 | 200/200 | 100/100 | 200/200 | 100/100 | 200/200 | 100/100 | |
| Number of output | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
| Activation function | Relu | Relu | Relu | Relu | Relu | Relu | Relu | Relu | Relu | Relu | |
| Optimizer control | Adam | Adam | Adam | Adam | Adam | Adam | Adam | Adam | Adam | Adam | |
| Epoch | 1,000 | 1,000 | 1,000 | 1,000 | 1,000 | 1,000 | 1,000 | 1,000 | 1,000 | 1,000 | |
| SVR | Kernel | Linear | Linear | Linear | Linear | Linear | Linear | Linear | Linear | Linear | Linear |
| C | 100 | 1,000 | 100 | 1,000 | 100 | 1,000 | 100 | 1,000 | 100 | 1,000 | |
| Gamma | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | |
| Epsilon | 1.98 | 2.98 | 1.98 | 2.98 | 1.98 | 2.98 | 1.98 | 2.98 | 1.98 | 2.98 | |
| SARIMA | p, d, q | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 | 0,1,0 |
| P, D, Q, s | 0,1,0,4 | 0,1,1,4 | 1,1,1,4 | 0,1,1,4 | 0,1,0,4 | 1,1,1,4 | 0,1,1,4 | 1,1,0,4 | 1,1,1,4 | 0,1,1,4 | |
| Akaike information criterion | 682 | 461 | 777 | 411 | 700 | 332 | 558 | 302 | 866 | 391 | |
| Bayesian information criterion | 681 | 464 | 782 | 415 | 701 | 337 | 561 | 305 | 863 | 395 | |
Note: Seq, Sequential; config, configuration.
3.3 Analysis and comparison of simulation results among ANN, SVR, and SARIMA models
With regard to the obtained results from the data prediction for land use in Table 3 and Figure 4, it can be verified that the best forecasting models are in Quangtri province. The second-best models are ThuaThienHue province. Danang, Quangbinh, and Quảngnam provinces show the predicted reliabilities to gain third, fourth, and fifth positions, respectively. The specific forecast results for two types of land use in each location are as follows. The SVR models for agriculture land use (with R 2 = 0.95, MAE = 1.03 [ha], and RMSE = 1.03 [ha]) and aquaculture land use (with R 2 = 0.93, MAE = 4.87 [ha], and RMSE = 5.01 [ha]) supply a better fit than the other models for Quangtri. In the implementation of the other models, the hierarchy shows to consider the order of SVRAgriculture_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use, and SVRAgriculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use. The land-use prediction for ThuaThienHue province shows that the SVRAgricultute_land_use and SVRAquacultute_land_use models are the best forecasting accuracy, and the hierarchical order of other models are ANNAgriculture_land_use > SARIMAAquaculture_land_use and ANNAquacultute_land_use > SARIMAAquaculture_land_use. The forecasting land-use models for Quangbinh and Danang indicating hierarchical order are SVRAgricultute_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use and SVRAquaculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use for Quangbinh, and SVRAgricultute_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use and SVRAquaculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use for Danang, respectively. At the same time, the model of SARIMAAquaculture_land_use with MAE = 39.66 (ha), RMSE = 85.44 (ha), and R 2 = 0.67 for Quangbinh is also the worst prediction compared to others. The prediction result of land use in Quảngnam shows that the order of hierarchical models with the accuracy of performance is SVRAgricultute_land_use > ANNAgriculture_land_use > SARIMAAgriculture_land_use and SVRAquaculture_land_use > ANNAquacultute_land_use > SARIMAAquaculture_land_use. In addition, the forecasting of SARIMAAgriculture_land_use model for Quảngnam gives the lowest accurate level with MAE, RMSE, R 2, and R values are 480 (ha), 610 (ha), 0.61, and 0.75, respectively.
Accuracy parameters for agriculture land-use and aquaculture land-use prediction using three models across five provinces
| Province | Parameter | Agriculture land use | Aquaculture land use | ||||
|---|---|---|---|---|---|---|---|
| SVR | SARIMA | ANN | SVR | SARIMA | ANN | ||
| Quangbinh | MAE (ha) | 3.41 | 85.75 | 36.97 | 3.87 | 39.66 | 13.40 |
| RMSE (ha) | 3.45 | 51.58 | 37.1 | 3.96 | 85.44 | 13.42 | |
| R 2 | 0.91 | 0.72 | 0.93 | 0.92 | 0.67 | 0.93 | |
| R | 0.92 | 0.85 | 0.94 | 0.92 | 0.73 | 0.92 | |
| Quangtri | MAE (ha) | 1.03 | 293.94 | 67.16 | 4.87 | 10.41 | 9.91 |
| RMSE (ha) | 1.03 | 649.75 | 67.51 | 5.01 | 25.32 | 9.91 | |
| R 2 | 0.95 | 0.71 | 0.94 | 0.93 | 0.81 | 0.93 | |
| R | 0.94 | 0.68 | 0.92 | 0.94 | 0.89 | 0.94 | |
| ThuaThienHue | MAE (ha) | 1.73 | 61.64 | 43.95 | 3.44 | 9.39 | 10.12 |
| RMSE (ha) | 1.72 | 166.84 | 43.94 | 3.63 | 25.01 | 15.29 | |
| R 2 | 0.94 | 0.72 | 0.92 | 0.94 | 0.87 | 0.88 | |
| R | 0.95 | 0.68 | 0.93 | 0.92 | 0.84 | 0.91 | |
| Danang | MAE (ha) | 3.01 | 178.31 | 42.23 | 8.75 | 9.01 | 12.52 |
| RMSE (ha) | 3.09 | 502.31 | 45.35 | 10.43 | 21.45 | 13.8 | |
| R 2 | 0.93 | 0.82 | 0.94 | 0.86 | 0.83 | 0.84 | |
| R | 0.94 | 0.73 | 0.95 | 0.89 | 0.79 | 0.81 | |
| Quangnam | MAE (ha) | 1.35 | 480 | 44.03 | 2.31 | 9.21 | 13.08 |
| RMSE (ha) | 1.38 | 610 | 45.15 | 2.45 | 21.27 | 13.11 | |
| R 2 | 0.94 | 0.61 | 0.91 | 0.93 | 0.72 | 0.91 | |
| R | 0.94 | 0.75 | 0.93 | 0.94 | 0.81 | 0.87 | |
Furthermore, the forecasting implementation of the models is also visualized and considered by the Taylor diagram. Taylor's diagram summarizes the St Dev and correlation coefficient (CC) that are comprised concomitantly in the assessment of the respective model [56,57]. The St Dev and CC between the observed and predicted datasets for all the land-use models of the provinces are described in the Taylor diagram, as shown in Figure 5. It may be observed for ANN, SVR, and SARIMA models in Quangbinh (CCSVR_agricuture_land_use = 0.92, CCSARIMA_agriculture_land_use = 0.85, CCANN_agriculture_land_use = 0.94, CCSVR_aquacuture_land_use = 0.92, CCSARIMA_aquaculture_land_use = 0.79, and CCANN_aquaculture_land_use = 0.92), in Quangtri (CCSVR_agricuture_land_use = 0.94, CCSARIMA_agriculture_land_use = 0.68, CCANN_agriculture_land_use = 0.92, CCSVR_aquacuture_land_use = 0.94, CCSARIMA_aquaculture_land_use = 0.89, and CCANN_aquaculture_land_use = 0.94), in ThuaThienHue (CCSVR_agricuture_land_use = 0.95, CCSARIMA_agriculture_land_use = 0.68, CCANN_agriculture_land_use = 0.93, CCSVR_aquacuture_land_use = 0.92, CCSARIMA_aquaculture_land_use = 0.84, and CCANN_aquaculture_land_use = 0.91), in Danang (CCSVR_agriculture_land_use = 0.94, CCSARIMA_agriculture_land_use = 0.73, CCANN_agriculture_land_use = 0.95, CCSVR_aquaculture_land_use = 0.89, CCSARIMA_aquaculture_land_use = 0.73, and CCANN_aquaculture_land_use = 0.81), and in Quảngnam (CCSVR_agriculture_land_use = 0.94, CCSARIMA_agriculture_land_use = 0.75, CCANN_agriculture_land_use = 0.93, CCSVR_aquaculture_land_use = 0.94, CCSARIMA_aquaculture_land_use = 0.81, and CCANN_aquaculture_land_use = 0.87). This diagram demonstrates that these models were optimal accuracies of almost all models’ outcomes were significantly closer to 1. Moreover, it can result in overestimation when the SD of the observation values is lower than the SD of estimation values and vice versa. On the other hand, the SARIMA model for agriculture land-use prediction with CCSARIMA_agriculture_land_use = 0.68 in Quangtri, CCSARIMA_agriculture_land_use = 0.68 in ThuaThienHue point out that the level of gaining accuracy is only above medium; at the same time, the lowest estimations for agriculture and aquaculture land-use changes are SARIMAAgriculture_land_use model with CC for Quảngnam, and model of SARIMAAquaculture_land_use for Quangbinh, respectively.
4 Discussions
The central coastal area has a slower socioeconomic development rate than that of Hanoi and Ho Chi Minh cities. Therefore, agricultural land use and aquatic land use in agriculture and aquaculture are still crucial sources of income for the people living in the region. The urbanization and industrialization process gradually narrow down the land-use squares in recent years. Therefore, the assessment, analysis, and forecasting of land use are very important for local authorities and stakeholders.
This study used SVR, SARIMA, and ANN models for agriculture and aquaculture land-use change in the Central Coast Region of Vietnam to assess the impact of urbanization speed and industrialization processing on the two types of land use. Three algorithms showed highly accurate results for estimating the land-use change, in which the SVR and ANN models indicated more accuracy for Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam, respectively, compared with the SARIMA model. In addition, the predicted values of land-use changes created by the SARIMA model also supply acceptable results.
In order to evaluate the level of predicted accuracy of these results, this study is compared with other studies based on the accurate parameters. Jamali (2019) [58] used ANN to predict land-use/land-cover mapping using Landsat 8 O.L.I. in the northern region of Iran. The RMSE and MAE values for the testing phase were 6.0 and 6.0, respectively, and the values of the two parameters are lower than this study’s results for ANN models. Talukdar et al. [59] employed ANN and SVR models to predict the land-use land-cover classification of the river Ganga from Rajmahal to Farakka barrage in India. The RMSE values of the two models were 0.09 and 0.11 for ANN and SVR, respectively. The parameter values are lower than this study’s results, in which the lowest values of accurate parameters are 9.91 and 1.03 for ANN and SVR models. Soltani et al. [60] deployed the SARIMA model to estimate lake surface area in the Tashk-Bakhtegan lakes, Iran. The simulation result showed that the RMSE = 70.217, MAE = 49.425, and the indicator values are equivalent to this study result.
Although classification accuracies for land use are still important for the provinces, these results may support the provinces’ authorities and other stakeholders in decision-making and planning regarding two types of land use. The result can help the Ministry of Natural Resources and Environment to carry out the land-uses inventory and upgrade agriculture and aquaculture land-use change maps every 5 years. At the same time, the DONREs use the predicting database and update the kinds of land-use change manually. Moreover, evaluating the land-uses change will give policymakers regarding labor reorganization in the agriculture and aquaculture fields, also changing production methodology.
5 Conclusions
In this study, the SVR, SARIMA, and ANN models are presented to forecasting land-use change in Quangbinh, Quangtri, ThuaThienHue, Danang, and Quảngnam provinces of Vietnam. The observed data consisting of agriculture and aquaculture land use were collected from the start of 2010 to the end of 2020 for prediction. The data is also divided into two phases as the training and testing phases occupied from first quarter to thirty-second quarter and from thirty-third quarter to forty-fourth quarter, respectively. The studied and observed values are compared using four widely used statistical indicators (i.e., RMSE, MAE, R, and R 2). The study results show that almost SVR models improve the accurate performance more than ANN and SARIMA in Quangtri, ThuaThienHue, Danang, and Quảngnam provinces. The ANN model indicates more accurate forecasting implementation than the SVR and SARIMA models in Quangbinh province. Hence, the SVR and ANN models may be successfully precited for better land-use change. Furthermore, predicting land-use change can support land planning and decision-making for the local authorities.
Acknowledgments
We are thankful to the Department of Civil Engineering at the National Kaohsiung University of Science and Technology, Taiwan, and Thu Dau Mot University in Vietnam.
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Author contributions: Conceptualization, material and methods, writing, original draft preparation: Nguyen Hong Giang; writing, review, editing: YuRen Wang.
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Conflict of interest: The authors declare no conflict of interest.
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Data availability statement: The data used to support the findings of this study are available from the corresponding author upon request.
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