A Hybrid Approach Using GIS-Based Fuzzy AHP–TOPSIS Assessing Flood Hazards along the South-Central Coast of Vietnam

: Flood hazards a ﬀ ect the local economy and the livelihood of residents along the South-Central Coast of Vietnam. Understanding the factors inﬂuencing ﬂoods’ occurrence potentially contributes to establish mitigation responses to the hazards. This paper deals with an empirical study on applying a combination of the fuzzy analytic hierarchy process (AHP), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), and a geographic information system (GIS) to assess ﬂood hazards along the South-Central Coast of Vietnam. Data are collected from focus group discussions (FGDs) with ﬁve communal authorities; a questionnaire completed by eight hamlet heads in the Phuoc Thang commune (Binh Dinh province); and documents, reports, and thematic maps provided from o ﬃ cial sources. A total of 12 maps of ﬂood factors are prepared. The results show that terrain elevation, creek-bottom terrains, high tide-induced ﬂooding area, and distance to water body are the main factors a ﬀ ecting ﬂood hazards. The An Loi hamlet faces the highest risk for ﬂoods, followed by Lac Dien, Luong Binh, and Pho Dong. The map of ﬂood hazards indicates the western part is assessed as low hazard, whereas the eastern part is a very high hazard area. The study ﬁndings show that the hybrid approach using GIS-based fuzzy AHP–TOPSIS allows connecting decision makers with the inﬂuencing factors of ﬂooding. To mitigate ﬂoods, both the Vietnam national government and the Binh Dinh provincial government should integrate natural hazard mitigation into socio-economic development policies.


Introduction
Flood hazards are primary hazards associated with exposure to water [1]. Flooding areas have water logging or soil erosion [2]. Storms and climate change increase flood hazards [3]. Consequences  In the Kon River basin, the Tuy Phuoc district is often most damaged by floods. Floods associated with tropical storms cause yearly damage to the local economy. Floods show characteristic features-high intensity, rapid propagation rate, and floodwaters rise and fall-within a short period of time. Floods occur in four periods of the year: (i) floods during May and June are small, mainly occurring in the river beds; (ii) floods in the period from August to September are single peak floods, at a larger scale, with less amplitude, while the amount of water in the rivers is limited; (ii) floods from October to November show the largest scale, and cause most of the damage; and (iv) floods in December to January are small, and cause small losses. A series of major floods occurred along the Kon River in 1987, 1999, 2009, 2013, and 2016. Floods on the Ha Thanh River in November 2009 and along the Kon River in November 2013 are listed as historic floods. The November 2013 flood caused the most serious damage: it inundated 24,281 households (which is 51.7% of total households in the basin) and 11,011.4 hectares of agricultural land (accounting for 40.7% of the total flooded area in the basin). The total damage is estimated at 5.9 million $US, in which most of the losses are for agriculture (2.3 million $US), the irrigation system (1.31 million $US), housing (1.26 million $US), and transportation infrastructure (0.67 million $US). The Phuoc Thang commune suffers most from floods in the Tuy Phuoc district. On average, from two to three floods occur each year with a depth of 1-3 m, for up to 2 weeks. Floods are a major threat to the local economy and to the lives of residents. In the December 2016 flood, three people were killed, two people were injured, 81 houses collapsed, 1850 houses were flooded, and 350 houses were isolated. During the floods, schools, clinics, embankments, and transportation were seriously damaged. The total monetary damage of this flood was estimated at 0.23 million $US [32].

Fuzzy Analytic Hierarchy Process
Suppose that a committee of l decision makers (DM t , t = 1, . . . , l) is responsible for assessing n interventions (A i , i = 1, . . . , n), based on m criteria (C j , j = 1, . . . , m). Assessing interventions is based on each criterion, while the weights of the criteria are represented as linguistic variables and presented in the form of triangular fuzzy numbers [33][34][35].
The calculated process of the fuzzy AHP method is summarized as follows [33].
Step 1. Calculate the value of fuzzy synthetic extent with respect to the value i where: Step 2. Calculate the probability to compare the relation between two fuzzy numbers where: Step 3. Calculate the probability of relation for a fuzzy number to be greater than other fuzzy numbers Appl. Sci. 2020, 10, 7142 5 of 21 Step 4. Calculate vector W' . , n; t = 1, 2, . . . .n and i t

Fuzzy TOPSIS
Step 1. Determine the decision makers' (DMs') ratings for each criterion Set x ijt = e ijt , f ijt , g ijt ) , i = 1, . . . , n, j = 1, . . . , m, t = 1, . . . , l, as the appropriate rating defined for alternatives A i , by decision makers D t , for each criterion C j . The average value of the rating, x ij = (e ij , f ij , g ij ), can be calculated as: where: Step 2. Normalize the average value of the decision makers for each criterion To ensure compatibility between values and units of ratings and weights, these values need to be normalized into comparable ranges. Suppose r ij = (a ij , b ij , c ij ) is the average value of alternative A i for criterion C j . The normalized value x ij can be calculated as: where: Step 3. Determine the normalized rating-weight value The normalized rating-weight value G i is calculated by multiplying the weight values of the criteria by the rating value of the solutions as: Step The fuzzy positive ideal solution (FPIS,A + ) and fuzzy negative ideal solution (FNIS,A − ) are calculated as: Appl. Sci. 2020, 10, 7142 6 of 21 Distance of each alternative A i , i = 1, . . . , n from A + and A − is calculated as: where: d + i is the shortest distance of the alternative A i and d − i is the longest distance of the alternative A i Step 5. Calculate the closeness coefficient The closeness coefficients (CC i ) used to determine the ranking order of the alternatives are calculated as: The higher a coefficient, the closer that alternative is to the positive ideal solution (PIS) and the further that alternative is from the negative ideal solution (NIS). From these closeness coefficients, the best alternative among the given ones is determined.
Following the fuzzy TOPSIS, the sensitivity analysis is conducted to determine the impact of different conditions of selected criteria (C j ) and decision makers (DM t ) and on ranking alternatives (A i ). Figure 2 shows the relationships between a general goal, 10 selected criteria (C i , i = 1...10), and 8 studied alternatives (A j , j = 1 . . . 8). The general goal is determining factors affecting the likelihood of flood hazards. Criteria consist of slope (C 1 ), terrain elevation (C 2 ), creek-bottom terrain (C 3 ), soil type (C 4 ), stream density (C 5 ), drainage channel density (C 6 ), distance to water body (C 7 ), high tide-induced flooding area (C 8 ), land use and land cover (C 9 ), and water storage by transportation corridors (C 10 ). These criteria define "vulnerability" in flood studies. The selection of the criteria is based on the quality and quantity of the available information. Alternatives reflect eight hamlets of the Phuoc Son commune including Khuong Binh (A 1 ), Pho Dong (A 2 ), Duong Thanh (A 3 ), Luong Binh (A 4 ), Tu Cung (A 5 ), An Loi (A 6 ), Lac Dien (A 7 ), and Thanh Quang (A 8 ).

Combining AHP-TOPSIS with a Geographic Information System (GIS)
An appropriate methodology for flood risk assessment uses aggregated values of inputs corresponding to their weights. The combination of MCDM and GIS showed its advantages in hazard risk assessment [36,37]. The hybrid approach using fuzzy AHP-TOPSIS originally demonstrates a logically and systematically quantitative framework to initially indicate critical issues, thereafter, assigning associated relative priorities to these issues, selecting the best compromised alternatives, and lastly establishing communication towards general acceptance. The fuzzy AHP, fuzzy TOPSIS, and GIS are integrated into the geographical fuzzy decision making. The fuzzy AHP is used to calculate the weights of selected criteria based on expert opinions as shown in a structured questionnaire. The fuzzy TOPSIS is applied to rank the flood hazardous risks of the alternatives using the weights of the criteria. GIS is used to conduct the Map Algebra procedure with the Spatial Analyst module of ArcGIS software and to map flood hazard factors and flood hazards. Figure 3 shows the logical framework of the flood risk assessment based on this approach. The first step is determining the criteria. This study uses 10 criteria, representing the terminology "vulnerability" in flood studies in the selected areas ( Figure 2). The method of fuzzy AHP combined with fuzzy TOPSIS estimates the weights of the aforementioned criteria, and allows one to prioritize alternative sites based on the intensity of flood hazards. ArcGIS is the final step to aggregate the analyzed outcome of the fuzzy AHP-TOPSIS in a map.

Combining AHP-TOPSIS with a Geographic Information System (GIS)
An appropriate methodology for flood risk assessment uses aggregated values of inputs corresponding to their weights. The combination of MCDM and GIS showed its advantages in hazard risk assessment [36,37]. The hybrid approach using fuzzy AHP-TOPSIS originally demonstrates a logically and systematically quantitative framework to initially indicate critical issues, thereafter, assigning associated relative priorities to these issues, selecting the best compromised alternatives, and lastly establishing communication towards general acceptance. The fuzzy AHP, fuzzy TOPSIS, and GIS are integrated into the geographical fuzzy decision making. The fuzzy AHP is used to calculate the weights of selected criteria based on expert opinions as shown in a structured questionnaire. The fuzzy TOPSIS is applied to rank the flood hazardous risks of the alternatives using the weights of the criteria. GIS is used to conduct the Map Algebra procedure with the Spatial Analyst module of ArcGIS software and to map flood hazard factors and flood hazards. Figure 3 shows the logical framework of the flood risk assessment based on this approach. The first step is determining the criteria. This study uses 10 criteria, representing the terminology "vulnerability" in flood studies in the selected areas ( Figure 2). The method of fuzzy AHP combined with fuzzy TOPSIS estimates the weights of the aforementioned criteria, and allows one to prioritize alternative sites based on the intensity of flood hazards. ArcGIS is the final step to aggregate the analyzed outcome of the fuzzy AHP-TOPSIS in a map.

Data Collection
Data were collected during four field trips in October and November 2018, and January and March 2019. These periods were selected because floods are the most severe from October to November, and the spring rice is the most impacted by floods. Flooded areas were detected ( Figure  4). Four focus group discussions (FGDs) were organized during the field trips with the involvement of the research team and five local authorities to determine flood hazard factors. After FGDs, 10 factors were selected and are included in a questionnaire sent to the committee members.

Data Collection
Data were collected during four field trips in October and November 2018, and January and March 2019. These periods were selected because floods are the most severe from October to November, and the spring rice is the most impacted by floods. Flooded areas were detected ( Figure 4). Four focus group discussions (FGDs) were organized during the field trips with the involvement of the research team and five local authorities to determine flood hazard factors. After FGDs, 10 factors were selected and are included in a questionnaire sent to the committee members. Figure 3. Logical framework of the study. AHP-analytic hierarchy process, TOPSIS-technique for order of preference by similarity to ideal solution.

Data Collection
Data were collected during four field trips in October and November 2018, and January and March 2019. These periods were selected because floods are the most severe from October to November, and the spring rice is the most impacted by floods. Flooded areas were detected ( Figure  4). Four focus group discussions (FGDs) were organized during the field trips with the involvement of the research team and five local authorities to determine flood hazard factors. After FGDs, 10 factors were selected and are included in a questionnaire sent to the committee members. Data for weighting criteria and prioritizing alternative sites in the Phuoc Thang commune were collected using a questionnaire with the committee of decision makers (DMs), including five village heads, who have lived for a long time in the local villages and are knowledgeable about various issues related to the local flood hazards. The questionnaire consists of three parts: (i) selecting factors affecting the likelihood of flood hazards and flood levels locally with "yes-no" questions, and then explaining the selections (Table 1); (ii) comparing 10 criteria by scoring the information of the respondents on the AHP's comparison matrix; and (iii) selecting alternatives using a Likert's 5-scale  Data for weighting criteria and prioritizing alternative sites in the Phuoc Thang commune were collected using a questionnaire with the committee of decision makers (DMs), including five village heads, who have lived for a long time in the local villages and are knowledgeable about various issues related to the local flood hazards. The questionnaire consists of three parts: (i) selecting factors affecting the likelihood of flood hazards and flood levels locally with "yes-no" questions, and then explaining the selections (Table 1); (ii) comparing 10 criteria by scoring the information of the respondents on the AHP's comparison matrix; and (iii) selecting alternatives using a Likert's 5-scale of agreement (1 = strongly disagree; 2 = disagree; 3 = undecided; 4 = agree; 5 = strongly agree). Respondents took approximately 30 min to complete the questionnaire.

Weights of Criteria
After determining the criteria assessing the factors that affect the likelihood of flooding (Figure 4), the committee members were invited to make a comparative pairwise assessment of the importance of the criteria. Based on the assessment scale and the selected factors, the committee members made a specific assessment and compared the pairs of criteria. The average assessment results of the comparison of criteria and comparison of pairs of criteria were calculated using the average results of the committee members. The respondents decide the level of flood hazard risks for the selected studied area. Table 2 shows the criteria weights calculated using fuzzy AHP with linguistic variables. Table 2. Linguistic variables and fuzzy numbers.

Linguistic Variables Fuzzy Numbers
Extremely important (EXI) (7,9,9) Very strongly important (VSI) (5,7,9) Strongly important (SI) (3,5,7) Medium important (MI) (1,3,5) Equally important (EI) (1,1,1) Table 3 shows the most important criterion was C 2 compared to others based on the evaluation of 5 decision matrices on 10 criteria. Criterion C 2 (terrain elevation) is considered "very strongly important" (VSI) to "extremely important" (EXI) compared to C 1 (slope), C 3 (creek-bottom terrain), C 4 (soil type), C 6 (drainage channel density), C 7 (distance to water body), C 8 (high tide-induced flooding area), and C 10 (water storage by transportation corridors) for five decision matrices (DM 1-5 ). Criterion C 3 (creek-bottom terrain) was assessed "very strongly important" to "extremely important" compared to C 1 (slope), C 4 (soil type), C 5 (stream density), C 6 (drainage channel density), C 7 (distance to water body), and C 8 (high tide-induced flooding) for the decision matrices DM 2-5 . Criterion C 4 (soil type) was assessed "very strongly important" compared to C 1 (slope) for the decision matrices DM 3 and DM 5 . Criterion C 5 (stream density) was assessed "very strongly important" to "extremely important" compared to C 1 (slope) and C 5 (stream density) for the decision matrices DM 1 and DM 3 . Criterion C 6 (drainage channel density) was assessed "very strongly important" to "extremely important" compared to C 1 (slope) for the decision matrices DM 1-3 and DM 5 . Criterion C 7 (distance to water body) was assessed "very strong important" to "extremely important" compared to C 1 (slope), C 4 (soil type), C 5 (stream density), and C 6 (drainage channel density) for the decision matrices DM 1 , DM 3 , and DM 4 . Criterion C 8 (high tide-induced flooding) was assessed "very strongly important" compared to C 1 (slope), C 5 (stream density), C 6 (drainage channel density), and C 7 (distance to water body) for the decision matrices DM 1-3 . Criteria C 9 (high tide-induced flooding) and C 10 (water storage by transportation corridors) were assessed "very strongly important" to "extremely important" compared to C 1 (slope) for the decision matrices DM 4 and DM 5 . Table 4 shows the weights of 10 criteria resulting from the fuzzy AHP analysis. Four criteria show weight values over 0.1: terrain elevation (w C2 = 0.123, rank first), creek-bottom terrain (w C3 = 0.118, rank second), high tide-induced flooding area (w C8 = 0.106, rank third), and distance to water body (w C7 = 0.103, rank fourth). The weight of criterion C 2 is the highest, which corresponds with the most important role of the terrain elevation among the factors affecting the likelihood of flooding locally. On the contrary, the weights of criteria C 1 and C 9 are lowest, which points to the less important roles of slope and land use and land cover contributing to the flood hazard risks in a plain area.    Table 5

Sensitivity Analysis
Results of a sensitivity analysis show the impact of different conditions on ranking alternatives (hamlets). Nine scenarios were conducted in connection with 10 selected criteria and 5 decision makers (DMs). Scenarios S 1-3 deal with different groups of decision criteria based on their characteristics (the land-induced criteria (C 1 : slope; C 2 : terrain elevation; C 3 : creek-bottom terrain; C 4 : soil type), the water-induced criteria (C 5 : stream density; C 6 : drainage channel density; C 7 : distance to water body), and the anthropogenic-induced criteria (C 8 : high tide-induced flooding area; C 9 : land use land cover; C 10 : water storage by transportation corridors)) corresponding to all five decision makers. Scenarios S 4-8 deal with all 10 criteria corresponding to all five decision makers. Table 6 and Figure 6 shows that fuzzy TOPSIS is more sensitive to the changes of land-induced criteria and the committee of decision makers in different scenarios. The second scenario using slope, terrain elevation, creek-bottom terrain, and soil type reflects a small variation between alternatives A 4 (the Luong Binh hamlet) and A 7 (the Lac Dien hamlet) compared to the current case. On the contrary, the differences in the ranking alternatives are very much when using the remaining criteria (water-induced and anthropogenic-induced criteria, C 5-10 ) or not using the assessments of all the decision makers. The alternative A6 ranks first in all scenarios which indicates that the An Loi hamlet is consistent with the variation of selected decision criteria and decision makers. Alternatives A 2 (Pho Dong), A 4 (Luong Binh), and A 7 (Lac Dien) change ranking positions, but are always in the group assessed as having the most hazardous flood risk in all scenarios. Table 6. Results of sensitivity analysis for different scenarios.

Scenarios Decision Criteria (Aggregated Criteria and Sub-Criteria) Decision Makers Rankings
Current case (CC) C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 , C 8 , C 9 , C 10 DM 1 , DM 2 , DM 3 , DM 4 Figure 6 shows that fuzzy TOPSIS is more sensitive to the changes of land-induced criteria and the committee of decision makers in different scenarios. The second scenario using slope, terrain elevation, creek-bottom terrain, and soil type reflects a small variation between alternatives A4 (the Luong Binh hamlet) and A7 (the Lac Dien hamlet) compared to the current case. On the contrary, the differences in the ranking alternatives are very much when using the remaining criteria (waterinduced and anthropogenic-induced criteria, C5-10) or not using the assessments of all the decision makers. The alternative A6 ranks first in all scenarios which indicates that the An Loi hamlet is consistent with the variation of selected decision criteria and decision makers. Alternatives A2 (Pho Dong), A4 (Luong Binh), and A7 (Lac Dien) change ranking positions, but are always in the group assessed as having the most hazardous flood risk in all scenarios. Figure 6. Results of sensitivity analysis for fuzzy TOPSIS method (where: CI is Consistency Index; CC is current case; S1-8 are scenarios 1-8; A1-8 are alternatives 1-8). Table 6. Results of sensitivity analysis for different scenarios.

Zoning Flood Hazardous Areas
The weighting of the factors conducted from fuzzy AHP was the basis to assess the likelihood of flood hazards (FHs) in the hamlets of the Phuoc Thang commune by the formula: The map of the likelihood of flood hazards shows that four regions obtain four levels of flood hazard risk: the most hazardous area is located close to the Dai An River, which is the lower part of the Kon River (Figure 7). The map with the geographic distribution of the flood hazards shows the consequences of the inclined direction of the terrain from west to east: the western part is assessed as low hazard, whereas the eastern part is a very high hazard area.   (S7)  C1, C2, C3, C4, C5, C6, C7, C8, C9, C10  DM4  A6, A2, A7, A4, A5,  A1, A8, A3  Scenario 8  (S8)  C1, C2, C3, C4, C5, C6, C7, C8, C9, C10  DM5  A6, A4, A5, A7, A8, A2, A1, A3

Zoning Flood Hazardous Areas
The weighting of the factors conducted from fuzzy AHP was the basis to assess the likelihood of flood hazards (FHs) in the hamlets of the Phuoc Thang commune by the formula: FH = 0.088 × C1 + 0.123 × C2 + 0.118 × C3 + 0.090 × C4 + 0.098 × C5 + 0.097 × C6 + 0.103 × C7 + 0.106 × C8 + 0.085 × C9 + 0.092 × C10 The map of the likelihood of flood hazards shows that four regions obtain four levels of flood hazard risk: the most hazardous area is located close to the Dai An River, which is the lower part of the Kon River (Figure 7). The map with the geographic distribution of the flood hazards shows the consequences of the inclined direction of the terrain from west to east: the western part is assessed as low hazard, whereas the eastern part is a very high hazard area.

Conclusion and Discussion
This paper applied a geographical fuzzy decision making methodology to identify the high-risk areas of flood risks in terms of multiple potential factors and experts' opinions. An integrated fuzzy AHP-TOPSIS and GIS allowed connecting decision makers with the factors influencing the floods. The systematic and logical approach allowed dealing with complicated multi-stakeholder and multicriteria decision issues by site-specific weighting of the factors that affect the likelihood of floods. The

Conclusions and Discussion
This paper applied a geographical fuzzy decision making methodology to identify the high-risk areas of flood risks in terms of multiple potential factors and experts' opinions. An integrated fuzzy AHP-TOPSIS and GIS allowed connecting decision makers with the factors influencing the floods. The systematic and logical approach allowed dealing with complicated multi-stakeholder and multi-criteria decision issues by site-specific weighting of the factors that affect the likelihood of floods. The model determines the normalized rating weight value, and the distance from the alternatives to the positive and negative ideal points. Determining the intensity of the influencing factors and the different sites in which the affected hamlets are at risk is vitally important.
The findings of this study provide evidence on the direct impacts of increasingly devastating floods and suggest the required policies for the development of adaptation and prevention measures for the local populations. Three critical problems in flood hazard assessment were considered in this study: drivers of flood hazards [38], affected areas [39], and zoning flood hazardous areas [40]. A hybrid approach using a GIS-based fuzzy AHP-TOPSIS was developed to solve these problems.

1.
Results of the fuzzy AHP show that terrain elevation and creek-bottom terrain are the most important drivers, followed by stream density, drainage channel density, water storage, soil type, slope, and land use or land cover. The topography of the studied areas is most important: the closer to the main river, the lower the terrain. Water flows from higher to lower elevations and, therefore, the terrain influences the surface overflow and the penetration. Flat regions at lower elevation potentially submerge faster than higher regions. Land use and land cover is least impacted. Most of the communal areas are agricultural land or built-up sites; therefore, their functions are not affected.

2.
Results of the fuzzy TOPSIS show that the hamlets of An Loi, Lac Dien, Luong Binh, and Pho Dong are the worst alternative sites. Because they are nearest to the negative ideal solution, it is necessary to pay more attention to flood hazards in these alternatives.

3.
Results of a sensitivity analysis show that the most important role of land-induced criteria such as slope, terrain elevation, creek-bottom terrain, and soil type, is in estimating spatial distributions of flood hazard risks. This finding implies that for the Phuoc Thang commune, particularly, and other coastal plains, generally, removing one or a few water-induced (stream density, drainage channel density, and distance to water body) and anthropogenic-induced (high tide-induced flooding area, land use land cover, and water storage by transportation corridors) factors would not significantly affect the final risk distributions of the flood hazards.

4.
Results of zoning flood hazardous areas based on the GIS show that hamlets near the Dai An River are flood-prone. The An Loi and Lac Dien hamlets either share their flat terrain or position close to the Dai An River, which determines their exposure to floods.
In this study, the results of modeling are well explained using both available spatial and nonspatial data, such as field trip studies, FGDs with the involvement of local authorities, semi-structural questionnaires completed by village heads, thematic maps, documents, and official reports provided from official sources. Drivers challenging floods along the coast of Phuoc Thang, located in the lower Kon River basin, should be considered at a regional scale and include tropical storms, regional land use land cover change, and urbanization. The Kon River basin is affected by tropical storms which cause rains and floods. During the storms, the intensity of the rain is very high [25][26][27]31]. Regional land use and land cover changes also contribute to local floods. As a result of over-exploitation, the natural forests decrease sharply, while the planted forest area increases rapidly [31]. Natural forests have been replaced by monoculture forests such as Acacia spp. and Eucalyptus with a 5-7-year forest business cycle. When plantation forests are cut, the bare land is left, and the water holding capacity is limited, which increases the flood risk downstream. Urbanization, transport development, and population growth affect the drainage and flooding over the basin. The lower part of the Kon River is intensely urbanized with the expansion of urban areas among Quy Nhon, An Nhon, and Tuy Phuoc. This reduces the water storage capacity and limits the drainage during floods. Industrial zones of Phu Tai, Nhon Hoa, and Nhon Binh and resettlement areas of Bac Ha Thanh, An Phu Thinh, and Dai Phu Gia limit partially the flood flow of the Kon River. Large development areas such as the SOS Children's Village, the Quang Trung University, the Binh Dinh College, and especially the National Highway 19 C involve terrain leveling; bridges and culverts along the National Highway 1A have been narrowed, which affects natural water drainage [25,32]. The system of reservoirs and dams contributes to the extended flooded areas in a short period of time. Flood discharge by hydroelectricity installations and irrigation reservoirs also affect flooding downstream [32].
In addition, a poor local economy limits investment in flood mitigation. The economy of Phuoc Thang commune is driven by rice production (1850 hectares of rice with a total rice yield of about 13,000 tons per year). Phuoc Thang has a low average yearly income per person (about 950 $US) [31].
In 2018, the commune counted 317 poor households [31]. This makes the commune fully dependent on the state budget for natural disaster response and prevention. Next to financial limitations, restrictions exist on diversifying industries and income for the community. The livelihoods of the locals depend on agricultural production. This results in low and varying incomes. Agricultural and commercial services are very limited. Therefore, increasing income, improving living standards, and strengthening flood resilience enhance the capacity of the community.

Policy Implications
The results indicate that the fuzzy AHP-TOPSIS is a promising methodology to make accurate and reliable predictions of flood hazards. The integrated fuzzy AHP-TOPSIS and GIS are also suggested to assess the flood hazards in areas with a lack of data. These results will benefit engineers, flooding analysts, policymakers, local authorities, and locals providing guidance of flood hazard assessments, which directly helps to define actions to prevent floods in the regions. Flood warning systems, provisional flood defenses, protection of valuable assets, and mobilization of emergency services should be developed to mitigate the impacts of flood hazards. The methodology improves flood hazard assessment in similar coastal regions as well as allows for the establishment of rescue plans which alleviate casualties and loss of assets.
To mitigate floods in the study area, both the national government of Vietnam and the provincial government in Binh Dinh should integrate natural hazard mitigation in socio-economic development policies. The main lines of these policies should entail: 1.
An institutional framework for integrated regional and local flood management. Improving the management capacity of emergency responses to floods and proactively coping with long-term flood risks of economic sectors allows minimizing flood effects and the damage they cause, develops sustainable production in the industry along the value chain, and strengthens regional integration to flood responses and mitigation by the river basin. Medium-and long-term priorities on flood risks for agriculture and drinking water supply should be identified for each locality in the Kon River basin.

2.
Strengthen flood forecast and warning capacity. The national government of Vietnam has invested in construction and transfer of material and technical facilities; human resources; forecasts in flood warnings; meteorological and hydro-meteorological observation stations; automatic rain gauges measuring the rainfall; and radar weather stations in Quy Nhon and Pleiku. Establishing flood observation stations at key points and places where monitoring stations are currently lacking to enhance the collection of real-time flood information is necessary. When floods occur, the Provincial Commanding Committee of Natural Disaster Prevention and Control, Search and Rescue directs the provincial radio and television channels and other information channels to implement an emergency response and communication plan, promptly conveying flood and storm forecasts and warning bulletins to localities and households in areas at risk of flooding. 3.
The irrigation system should allow for the response to flood hazards. The reinforcement, maintenance, and protection of dykes, dams, irrigation works, and canals in rice fields should be strengthened before flooding periods. Small-and medium-sized reservoirs, rivers, and sea dike systems should be built. Irrigation management and exploitation units regularly monitor and inventory water sources, formulate economical irrigation plans, take initiatives on implementing economic irrigation solutions, and have water distribution plans to ensure water supply for essential needs such as daily life, husbandry, and high-value crops. 4.
Complete regional industrial and sectoral planning. The spatial organization of industry, services, agriculture, forestry, and fisheries of the basin and localities should follow the principles of natural disaster adaptation and mitigation.

5.
Strengthen the management and protection of critical forests and watershed forests. An efficient solution for flood hazard mitigation for the lower Kon River, generally, and for the studied Phuoc Thang commune, particularly, is to protect and develop forests. Strengthen the supervision on afforestation, ensure the proper implementation of the afforestation plan, develop large timber forests, and increase the value of forest timber. Increase the watershed forests as they are a major factor limiting the amount of water and reduce the flood intensity and flooding levels in the lower Kon River. Funding: This research was funded by the Vietnam national project code TN18/T11.