In recent years, natural disasters have occurred more and more frequently with increasingly strong impacts because of climate change. Faced with this situation, scientists are constantly researching and applying new technologies to improve the capacity to respond to natural disasters. With increasingly developed technology, time-series and monitoring subsidence warning systems are increasingly in the world (
Hu et al., 2019
;
Ochungo et al., 2019
;
Fadhillah et al., 2020
;
Franczyk et al., 2022
;
Song et al., 2023
;
Salameh et al., 2024
;
Jena et al., 2024
). As a landslide warning system in India with four functional modules (
Ghosha et al., 2010
), the system provides warning information in the form of short messages with a maximum delay of 50 seconds and a minimum of 10 seconds. The landslide warning system in Bangladesh (
Mallick & Parvin, 2019
) includes two components: one part uses GIS tools to show changes in the shoreline and the second part is a household survey process with a number of pre-prepared question structures.
Hemmelder et al. (2018)
proposed a riverbank erosion warning system in France that built a map using multi-temporal imagery from unmanned aerial vehicles to assess river morphology and riverbank erosion in the Buëch River floodplain in the Hautes-Alpes province in southeastern France. The early warning system for subsidence in southwestern Iran (
Pourmorad et al., 2021
) made a subsidence risk map using an integrated hierarchical analysis method (AHP and FAHP) and programming in the MATLAB environment with the help of the Evalfs function.
Gao et al. (2021)
proposed a study on a subsidence warning system in China based on the application of GIS technology, combined with surface subsidence analysis, the values of surface subsidence, slope, curvature deformation values, horizontal displacement, and corresponding horizontal deformation formulas were comprehensively analyzed and studied. In order to visualize the surface deformation of the subsidence area, this study introduces a 3D visual settlement data analysis method and performs the corresponding regression visualization and calculation result analysis in the subsidence area depth prediction process.
Ning et al. (2023)
integrated multi-source monitoring, early warning technology, and multi-pronged prevention and control technology. It can monitor and control ground subsidence and early warning prevention in Beijing, Tianjin, and Hebei regions.
Chen et al. (2024)
employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nanjing, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in the Yangtze River.
In Vietnam,
Minh et al. (2018)
established an early warning system based on the global ASIS system. The ASIS system was independently developed for Ninh Thuan province. The system has produced highly accurate drought monitoring results consistent with historical drought events in Ninh Thuan. This system can help support decision-making in the optimal operation of the irrigation system in Ninh Thuan as well as overcoming problems caused by drought. The flood monitoring and warning system on the WebGIS platform (
Phuong et al., 2022
) has been built with three main components: 1) monitoring and surveillance system, 2) information storage, processing, and forecasting system, 3) information provision and emergency response support system and the drought monitoring and warning system on the WebGIS platform has been built with the components: a) standardized rainfall index SPI, b) moisture deficit index SWDI, c) reservoir water level index.
Quang et al. (2021)
has presented the results of the research on building an early warning system for landslides and flash floods based on IoT and WSN technology. This system has provided a solution for monitoring and early warning of slope instability and measuring the movement of mudflows based on the latest technology integrating pore water pressure sensors, acoustic emission measurement, inertia, low-power wide area networks, artificial intelligence on super energy-saving microprocessors, etc., an effective approach to detect slope movements and mudflows and then notify/alarm users. The early warning system for landslides in the coastal area of the Mekong Delta (
WRI, n.d.
) includes a data center, rain gauge station, surveillance camera, vibration sensor, and radar water level meter. The technology applies a 24/7 monitoring system and is easy to operate. The sensor combined with the monitoring information platform is integrated with the flood forecasting system. Therefore, users can access data from computers or update data via mobile networks connected to the Internet. After analysis and processing, data will provide a natural disaster warning threshold and send it to management agencies to be more proactive in the early detection of risks and hazards, helping to reduce damage to property and people as well as protect natural resources and the environment.
Subsidence is a fairly common phenomenon in Vietnam, especially in the Ca Mau Peninsula where there is almost no exogenous flow to supply water. The Ca Mau Peninsula’s main water supply uses underground water sources while the drought has increased and lasted for many years, leading to increasingly serious subsidence in this area. Nevertheless, it is evident that there is currently no early warning system for the risk of subsidence due to drought and groundwater exploitation. The increased subsidence has affected the socio-economic development in the area, so it is necessary to research and build a warning system for subsidence due to drought and groundwater exploitation.
2. Materials and Methods2.1. Description of the Study Site
The Ca Mau Peninsula is in the southernmost part of the country, with geographical coordinates from 8˚34' to 9˚33' north and from 104˚43' to 105˚25' east, with three sides bordering the sea. The eastern region of the Ca Mau Peninsula borders the East Sea, the Western and Southern regions border the Gulf of Thailand, and the northern region borders Bac Lieu and Kien Giang provinces (
Figure 1
). The Ca Mau Peninsula has a natural area of 522,119 hectares, accounting for 13.13% of the Mekong Delta area and 1.58% of the country’s area. The coastline is 254 km long; the west coast is 147 km long, and the east coast is 107 km long. Collapse and subsidence during the dry season are common in the freshwater region of Ca Mau Province. Therefore, this study focused on identifying the main factors influencing subsidence, using the AHP method to determine the weights of influencing factors and applying GIS technology to compile maps of locations at risk of subsidence in Ca Mau province.
Figure 1. Study location map.
2.2. Data and Method
The data used in the article include:
a) Application of AHP method (
Cheng et al., 2002
;
Saaty, 2008
) to determine factor weights: The factors affecting subsidence due to drought and groundwater exploitation in the study area were selected are: 1) geological structure; 2) Soil documents; 3) Groundwater flow exploitation; 4) Water flow in the dry season; 5) Current land use status; 6) Evaporation in the dry season. This article used AHP to calculate the weights of factors affecting subsidence in the study area.
b) Applying GIS to overlay factor map layers to build a subsidence risk map: 1) GIS technology is applied to edit component map layers; 2) Overlay component map layers with corresponding weights to edit the subsidence map in the study area.
c) Applying Python and Dart programming languages to build a warning system: 1) Python programming language is used to calculate and process results to build a warning system for the risk of subsidence due to drought and groundwater exploitation in the study area; 2) Dart programming language is used to build the user interface and display results of drought subsidence warning systems and groundwater exploitation for the study area.
Figure 2. Structure of the subsidence risk warning system.
3. Results and Discussion3.1. Results of AHP Application to Calculate Factor Weight
The paper selects 6 main factors affecting subsidence in the study area to evaluate the influence weight, the selected factors are presented in
Table 1
.
<xref ref-type="bibr" rid="scirp.135944-"></xref>Table 1. Selected criteria for assessing the risk of land subsidence.
No.
Selection criteria
Data sources used
1
Geological structure
Geological map of the study area (scale 1:10,000)
2
Soil documents
National Center for Water Resources Planning and Investigation
3
Groundwater flow exploitation
National Center for Water Resources Planning and Investigation
4
Water flow in the dry season
Calculated according to
SCS (Curve Number Method, 1964
,
1972
)
5
Current land use status
Land use map of the study area (scale 1:10,000)
6
Evaporation in the dry season
Calculated according to Penman-Monteith formula (
Allen et al., 1998
;
FAO, 2000
)
The weights of factors affecting subsidence in the Ca Mau Peninsula area are shown in
Table 2
.
<xref ref-type="bibr" rid="scirp.135944-"></xref>Table 2. Weight matrix of evaluation factors.
Element
Land
Qdry
Geological
Land use
ETdry
Qexploit
Wi
Land
0.033
0.125
0.012
0.014
0.012
0.024
0.036
Qdry
0.100
0.375
0.110
0.293
0.261
0.639
0.296
Geological
0.100
0.125
0.037
0.011
0.017
0.024
0.052
Land use
0.233
0.125
0.329
0.098
0.012
0.071
0.145
ETdry
0.233
0.125
0.183
0.293
0.087
0.030
0.159
Qexploit
0.300
0.125
0.329
0.293
0.609
0.213
0.312
The result shows that CI = 0.101 and CR = 8.1%. As the CR value is less than 10%, the weight can be used.
The subsidence risk index of the study area is calculated according to the following formula:
(1)
Formula (1) weighted with the influencing factors (land, Qdry, geological, land use, ETdry, groundwater exploitation) was applied to build a land subsidence risk zoning map, as shown in
Figure 2
. The study results are shown a subsidence risk zoning map that provides an intuitive and general overview of the areas with high subsidence risk and can be a document to help managers consult in the work of building a socio-economic development plan for the Ca Mau Peninsula region.
3.2. Results of Applying GIS technology to Determine Map Layers of Factors Affecting Subsidence
Hydro-meteorological data at stations in the study area were collected from six meteorological gauges (Ca Mau, Bac Lieu, Soc Trang, Vi Thanh, Tra Noc, and Can Tho) and sixteen hydrology gauges (Thot Not, Bon Tong, Tan Hiep, Co Do, Ha Giang 1, Xeo Ro, Vi Thanh, Tra Noc, Tan Quy Tay, Dai Ngai, Phung Hiep, Tran De, Phuoc Long, Ganh Hao, Nam Cam and Song Doc) (
Figure 3(a)
). These data were collected to calculate the flow and the amount of evaporation in the dry season in the study area. The data were collected from the Information and Data Center, Vietnam Meteorological and Hydrological Administration, Ministry of Natural Resources and Environment (MoNRE).
Geological, soil, and land use maps are presented in
Figure 3(b)
,
Figure 3(c)
,
Figure 3(e)
.
Groundwater exploitation documents were collected from the National Center for Water Resources Planning and Investigation (NAWAPI) (
Figure 3(d)
).
Figure 3. (a) Hydro-meteorological data at stations in the study area, (b) geological, (c) soil, (d) groundwater flow exploitation, (e) land use maps (Tri et al., 2024).--Figure 3. (a) Hydro-meteorological data at stations in the study area, (b) geological, (c) soil, (d) groundwater flow exploitation, (e) land use maps (Tri et al., 2024).
Figure 3. (a) Hydro-meteorological data at stations in the study area, (b) geological, (c) soil, (d) groundwater flow exploitation, (e) land use maps (Tri et al., 2024).--Figure 3. (a) Hydro-meteorological data at stations in the study area, (b) geological, (c) soil, (d) groundwater flow exploitation, (e) land use maps (Tri et al., 2024).
3.3. Early Warning System for Subsidence Risk Due to Drought and Groundwater Exploitation
The system after construction has the interface as shown in
Figure 4
.
The main modules of the system include:
Figure 4. Interface of the warning system for the risk of subsidence due to drought and groundwater exploitation.
The warning system for the risk of subsidence due to drought and groundwater exploitation after construction has been tested with the drought period of 2024. Below are the results of the system testing.
3.4. System Test Results
The system has been tested for the dry season in 2024. The subsidence risk map results from the system were compared with the subsidence location of the research area that was surveyed.
Table 3
is the actual surveyed subsidence location in the dry season in 2024.
<xref ref-type="bibr" rid="scirp.135944-"></xref>Table 3. Subsidence locations in the study area.
No
Subsidence locations
Characteristic
1
Rach Ruong Canal, Khanh Loc Commune, Tran Van Thoi District
Subsidence of about 500 m (new canal and road built in 2023, about 1km long)Subsidence just occurred in 2024, never occurred before
2
Canal No. 2, Khanh Loc Commune, Tran Van Thoi District
A section of road subsided about 30 mThe subsidence just occurred in 2024, never happened before
3
Rach Ruong C, Khanh Loc Commune, Tran Van Thoi District
Subsidence of about 50 m roadSubsidence occurred in 2024, never happened before
4
Hao Sai Canal, Khanh Loc Commune, Tran Van Thoi District
A road section of about 200 m has subsidedThe subsidence just occurred in 2024, never happened before
5
Nha May Canal, Khanh Hung Commune, Tran Van Thoi District
Subsidence of about 70 m roadSubsidence just occurred in 2024, never happened before
6
Hang B Canal, Khanh Hung Commune, Tran Van Thoi District
A section of road subsided about 2 5mThe subsidence just occurred in 2024, never happened before
7
Khanh Hung Commune, Tran Van Thoi District
A road section of about 100 m subsidedThe subsidence just occurred in 2024, never happened before
8
Cong Nghiep B Canal, Hung Commune, Tran Van Thoi District
A road section subsided about 60 mNew subsidence occurred in 2024, never happened before
9
Ngang Canal, Trum Thuat B Hamlet, Khanh Hai Commune, Tran Van Thoi District
A road section subsided about 50 mNew subsidence occurred in 2024, never happened before
10
Khanh Hung A Canal, Khanh Hung A Hamlet, Khanh Hai Commune, Tran Van Thoi District
A road section subsided about 30 mNew subsidence occurred in 2024, never happened before
The comparison results are shown below in
Figure 5
. It is evident that the computation results when compared to real survey data collected in the research area, are reasonably accurate in terms of the sites of subsidence that happened during the dry season of 2024 at certain points. The map of subsidence risk, which is the outcome of a warning system for the risk of subsidence due to drought and groundwater exploitation that has been tested, shows that the 10 points in the study area where subsidence actually occurred during the dry season of 2024 all correspond with areas with high risk of subsidence (orange).
As a result, a warning system for the possibility of landslides and subsidence in the Ca Mau peninsula region has been constructed using calculating software.
4. Conclusion
In recent years, the impact of climate change has become increasingly severe in Vietnam in general and the Ca Mau peninsula in particular. Drought is increasingly severe, leading to a serious shortage of fresh water in the study area. As a result, groundwater is increasingly overexploited in the area, leading to increasing subsidence. This is one of the urgent issues not only for the study area but also for the whole society. A warning system for the risk of subsidence due to drought and groundwater exploitation has been built and validated for the dry season in 2024. The results of the system have provided a map showing the location of the
Figure 5. Comparison of actual subsidence locations and subsidence locations from warning system.
potential for subsidence in the dry season in 2024, which is relatively accurate when compared with the subsidence points that were investigated and surveyed later. The warning system is built based on the factor weighting method combined with GIS technology. This system can be replicated in areas facing increasing subsidence.
During the operation of the warning system for the risk of subsidence due to drought and groundwater exploitation, users can continue to improve the accuracy of the system results by changing the factor weights to suit the impacts of climate change as well as socio-economic development activities. This is one of the main advantages of this system. The research results will be continuously adjusted to improve accuracy.
The main contribution of this study is to build a subsidence risk warning system for the Ca Mau Peninsula, where subsidence is increasingly serious and there is no early warning system for this phenomenon. The system contributes to supporting forecasters in warning of the risk of subsidence.
Acknowledgements
This research was funded by the ministerial-level project of MoNRE “Study on the scientific basic and establishing a warning system for the risk of landslide, subsidence due to the drought and underground water exploitation at Ca Mau Peninsula”, grant number: TNMT.2023.06.12 during 2023-2025. The ministerial-level key science and technology program on forecasting and warning of hydrometeorological disasters for disaster prevention and control in the 2021-2025 period. Program code: TNMT.06/21-25.
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