A Fuzzy-based method for artificial reefs site selection- Case Study: Kish Island, the Persian Gulf

doi:10.21203/rs.3.rs-3763985/v1

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A Fuzzy-based method for artificial reefs site selection- Case Study: Kish Island, the Persian Gulf

https://doi.org/10.21203/rs.3.rs-3763985/v1

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Coral reefs face numerous threats from natural and anthropogenic stressors, impacting their health and associated ecosystems. This study investigates a method for optimal artificial reef (AR) deployment around Kish Island (KI) in the Persian Gulf (PG) to mitigate environmental impacts caused by marine tourism and safeguard coral reefs. Utilizing geospatial information systems (GIS) and fuzzy-based analyses, diverse criteria (such as water depth, clarity, proximity to natural reefs, and species diversity) were assessed for site selection. Data from various sources, including field observations, existing maps, and high-resolution satellite imagery, were accurately collected and processed. The method enabled us to take into account the complex and uncertain nature of the marine environment in KI and provide more precise and accurate recommendations for AR deployment. Fuzzy overlay analysis integrated these criteria, resulting in a final suitability map for AR deployment. The southeastern areas around KI emerged as highly suitable locations, considering factors like coral reefs' proximity and diving club distances. Notably, this method's application differed from prior studies, showcasing its effectiveness in assessing site suitability for AR deployment. While the study focuses on AR for marine tourism and reef conservation, its fuzzy-based approach allows flexibility in considering uncertain environmental factors, aiding in sustainable marine resource management in the PG. Overall, the fuzzy-based method presented in this study could be a valuable tool for policymakers and environmental managers in the PG to make informed decisions about AR deployment and the sustainable management of marine resources. The method can also be adapted for use in other marine environments elsewhere in the world.

Fuzzy logic analysis

Coastal management

Coastal ecosystems

Coral reefs

Coral reefs are among the most vulnerable ecosystems that are affected by various damaging parameters, both natural and anthropogenic (Souter et al. 2021). The global threats from climate change (e.g., ocean acidification, rising sue sea surface temperature) as well as local threats including coastal development, sea-based pollution, land-based input of nutrients and sediments, overfishing, and destructive fishing practices are typical anthropogenic pressures that decline the health of coral reefs and their associated organisms (Souter et al. 2021). Among these pressures, the increase in water temperature, mostly resulting from climate change, is the primary natural factor that causes coral bleaching (Eakin et al. 2019). These stressors can damage and decline coral assemblages, with consequences for associated fish and other aquatic life.

Marine tourism is the largest sector contributing to the ocean economy and with 8.9–13.6 million marine diving tourists support up to 124,000 jobs worldwide (Schuhbauer et al. 2023). Yet, marine tourism can introduce pollution and destroy coral structures due to boat traffic noise and anchoring (Ferrari et al., 2018). SCUBA diving as the major activity in marine tourism brings economic benefits to the coastal cities and can transform human-nature relationships with coral reefs (Eider et al., 2021). However, the diving sector per se is at the forefront of transformative change for local and global ocean equity and sustainability. Therefore, it is necessary to find alternative solutions to minimize the environmental impacts of SCUBA diving on natural reefs. One of the most widespread approaches is the deployment of artificial reefs (AR) in nearshore areas. However, several procedures and precautions should be taken into account before, during, and after the deployment of AR in a selected area (Feary et al., 2011).

Fabi et al. (2015) published a practical guideline for the use of AR in the Mediterranean and the Black Sea, describing the essential actions in three phases: pre-construction, construction, and post-construction. The pre-construction phase aims to assess the validity and feasibility of AR construction in the selected area, including evaluating the possible interaction of the new reef and the natural ecosystem, potential alternatives to reef construction, and the physical structures of the selected site (e.g., water depth, sediment grain properties, current situation, and wave condition). Moreover, it should be ensured that the selected materials used for the construction of AR do not enter the dumping area and pollute the marine environment. In the construction phase, AR should be precisely deployed in optimal locations and then marked with buoys, with the final location of the deployed AR and safe navigational depths added to navigational maps. Finally, in the post-construction phase, the management sustainability of the deployed AR must be considered to adjust their benefits for all users and minimize conflicts among them.

In the Persian Gulf area (PG), few projects and studies have been undertaken on the site selection of AR. Bartholomew et al. (2022) assessed the challenges related to the deployment of AR in the PG and provided recommendations for decision-makers. They highlighted some advantages and disadvantages of the deployment of AR in this region, including increasing short-term fishing rates, tourism growth, protecting biodiversity, and providing ecological services as pros, while overfishing in the long-term, spreading invasive species, and changing benthic habitat around the AR as cons. They also warned that the deployment of AR might trigger the destruction of natural coral reefs if they are considered surrogates for natural habitats. Mousavi et al. (2015) investigated the suitability of a spatial multi-criteria decision-making tool for the site selection of AR in KI in the northern PG. They further employed the Weighted Linear Combination (WLC) and Analytical Hierarchy Process (AHP) to identify and prioritize the most suitable areas for the deployment of AR. In doing so, they considered physical (e.g., water depth and water temperature), biological (e.g., larval dispersal), and managerial (e.g., distance from natural reefs) criteria to select suitable areas for deployment of AR. Azhdari et al. (2012) argued that deploying AR in the form of a mixture of Reef Balls and fish nests in Bandar Lengeh in the northern PG had boosted the fish community.

This study aims to propose a method for selecting optimal locations for the deployment of AR around KI in the northern PG. In doing so, various data, information, and maps were employed, including existing topographic maps, high-resolution satellite imagery, drone-based mapping, and data collected during field measurements. GIS (Geospatial Information System) analysis was used to create zoned maps based on several criteria consisting of distances from diving clubs, water depth, water clarity, sea bottom type, current speed, and direction, proximity to natural coral reefs, species diversity of benthic organisms, percentage of coral coverage, and diversity and abundance of fish in nearby coral patches. Thereafter, fuzzy-based analyses were conducted to prioritize the high-scored locations and identify the most optimal ones. This method can assist decision-makers in selecting the best locations based on desired criteria, interests, and/or expedients. Although this method has been widely used in various fields, such as engineering, management, finance, and environmental sciences, to make effective decisions in complex and uncertain environments, it has not yet been used for site selection for the deployment of AR, therefore, this research aims to examine its effectiveness in this context.

2.1. Study area

The study area for this research was Kish Island (KI) located in the northern PG (Fig. 1). KI was selected due to its status as one of the most frequently visited Iranian islands in the region, and its importance as a habitat for relatively high coral species diversity (Samimi Namin et al., 2009; Shokri and Mohammadi 2021). In KI, 20 active diving clubs provide various water recreational services, including SCUBA diving, parasailing, and jet skiing, and most of these activities are conducted in coral reef sites. As a result, different contamination and pollutants enter the sea environment (Beni et al., 2021). KI has a total area of ~ 90 km2 and a perimeter of 40 km. The air temperature in KI ranges from 15°C in the cold month of February to 40°C in the warm month of August (Kabiri et al., 2013). The seawater temperature fluctuates between 22 and 33°C throughout the year, and the maximum tidal range is ~ 1.8 m (Kabiri et al., 2012). Previous studies have shown that the Secchi disk depth (SDD) values, which indicate water clarity, vary between 1.5 and 20 m in the PG (Kabiri and Moradi, 2016; Kabiri, 2023) while it fluctuates between 5 and 15 m around KI (Kabiri, 2022a). Coral reefs around KI appear as unconnected patchy reefs mostly on the east, north, and southeast of the island at a depth of 3 to 18 meters (Shokri and Mohammadi, 2021) and mostly dominated by Porites and Acropora corals (Kabiri et al., 2014; and 2020; Shokri and Mohammadi, 2021). Due to the proximity of the major coral reefs to the shore (i.e. ≈10 m), SCUBA divers can reach the corals through shore diving; however, for safety reasons, amateur divers are strongly recommended to undertake the diving by boat (Shokri and Mohammadi, 2021).

Figure 1

2.2. Types of data and information

This study utilized three sources of data and information: (i) existing maps and historical data, (ii) in-situ data collected during field measurements and observations, and (iii) information extracted from a high-resolution satellite image. Before these data being used in further analysis, all of them must be pre-processed and prepared in advance. Thereafter, a fuzzy-based method was employed to determine the optimal areas for the deployment of AR around KI. The detail of the obtaining and preparation of the data and information will be discussed in detail in the following sections.

2.2.1. Collecting the existing maps and data

One of the main and primary sources of data in this study was a bathymetric map of KI (Fig. 2), which was used to create a clustered map based on depth values. Moreover, this data is essential for conducting water column corrections on satellite image before it can be used for classification processing. Also, the location of active diving clubs around KI (Fig. 2) was obtained and then utilized for further GIS-based analysis.

Figure 2.

Another source of ancillary data used in this study was a detailed map (Fig. 3a) of the coral reefs at the Simorgh reef in southeastern KI (Fig. 3b). This map was produced using drone-based aerial photography and incorporated information about the coral types and even bleached corals on the date of photography. Although the mapped area was limited, it was used as required training data for classification processes when analyzing high-resolution satellite images. The procedure used to create this map is described in detail by Kabiri et al. (2020).

Figure 3.

This study also utilized data of the current speed and direction of the waters surrounding KI, obtained from a study conducted by Yari et al. (2020) in which a Teledyne RDI Acoustic Doppler Current Profiler (ADCP) was used to measure the velocity and direction of currents. While these data do not cover the northern part of KI, it shows the pattern of high-tidal and low-tidal currents in KI (Fig. 4a and 4b, respectively). In addition to considering the current speed in further analysis, the current orientation was also taken into account when scoring the areas based on their location, whether they were in the path of currents coming from nearby coral reefs or not.

Figure 4.

The status of species diversity of mega benthic organisms (e.g., sea cucumber, sea stars, mollusks, worms, shrimps, crabs) and the percentage of coral coverage, as well as the diversity and abundance of reef fish in nearby coral patches, were used in this study as potential criteria reflecting the health and diversity of locations. These data were collected from existing sources and then rearranged based on the GIS-based standards being set for this research. The data were collected using various types of field observations, including manta tow survey, photo quadrat, underwater visual surveys, and fish traps and nets.

2.2.2. Field observations

To collect the necessary data and information, various methods of field measurements and observations were employed, such as SCUBA diving and snorkeling photography, CTD measurements, and observations of Secchi disk depth (SDD) as described as followings:

2.2.2.1. Underwater SCUBA diving photography

This method was undertaken in situations where benthic habitats and features could not be observed from the surface of the water. This method provides precise data on benthic habitats, as the diver is in direct contact with benthic habitats while recording data and metadata on coral growth forms, reef type, and health status of coral reefs. To implement this method, a photo-cam, and GPS clocks must be precisely synchronized (Fig. 5a). During these observations, the GPS tracking option was set to a 1-second time interval to obtain coordinates of the underwater photos in the post-processing step. The GPS was covered with a waterproof pack and attached to a floatable buoy. The diver was then connected to the buoy using a rope and adjusted the length of the rope was based on the diving depth to keep the GPS afloat and perpendicular to the diver (Fig. 5b). The locations of the taken photos were obtained from the GPS tracking records and acquisition time. The map generated by this technique was a point-wise map that provides information about the important benthic habitats and features in deeper areas.

Figure 5.

2.2.2.2. Snorkeling photography

The principles of snorkeling photography are similar to underwater photography, but it does not require the diver to dive to a certain depth. This method is suitable when benthic habitats and features are visible from the surface of the water. To implement this method, a GPS (Fig. 6a) is tied to the diver with a rope (Fig. 6b), and the diver swims on the surface of the water (Fig. 6c) and takes several overlapping photos (Fig. 6d) along a line transect. Conducting this method during low-tide and calm weather with high seawater clarity yields better results. After extracting the coordinates of each photo, geo-referenced mosaics were generated from each photo. Different types of benthic habitats and features, such as live and dead corals and rocks, were then digitized to produce topologic maps that could be used for further analysis.

Figure 6.

2.2.2.3. CTD measurements and SDD observations

During field observations carried out on November 17, 2020, a small boat was used to conduct CTD measurements and SDD observations around KI. The distance between the measurement points was set to be between 500 and 1,000 meters, as shown in Fig. 7. At each of these points, a Sea & Sun Tech CTD device (Fig. 7a) was used to measure various parameters, including salinity, water temperature, chlorophyll-a (Chl-a), water pressure (used to calculate water depth), dissolved oxygen (DO), water turbidity, and pH from the water surface (Fig. 7b) to the sea bottom (Fig. 7c). To produce the final maps, the mean values of each parameter were calculated for the water column and used for further analysis. This approach ensures that the overall trends and characteristics of the water column are captured in the final maps, providing a more accurate representation of the data. Additionally, a simple Secchi disk (Fig. 7d) was used at each point to observe water clarity. The coordinates of all points were recorded using a Garmin™ 78s hand-held GPS. The SDD observations were conducted by a certain person, the first author, to maintain consistency across measurements. To avoid the effects of water surface reflections, readings were taken on the shadow side of the boat, as recommended by Tyler (1968) and Kabiri (2022b). It should be noted that SDD observations were not valid at points where the seabed was visible from the surface, resulting in fewer observations of SDD values compared to CTD measurements. The total number of CTD measurements and SDD observations were 130 and 96, respectively (Fig. 7e).

Figure 7.

2.2.3. Remotely sensed data

A cloud-free WorldView-2 (WV2) satellite image from October 23, 2020, was employed for mapping the benthic habitats and features around KI. The detailed specifications of this image are presented in Table 1. Typically, some pre-processing is required before performing the main processes, such as radiometric and atmospheric corrections of the raw satellite image. For this study, the ACOLITE open-source module (version: 20220222.0) was selected specifically to execute these corrections. This module applies the dark spectrum fitting approach and is initialized with the assumption that the atmosphere is homogeneous over a captured scene. Subsequently, the atmospherically corrected reflectance values of all visible bands of the WV2 image were determined for each pixel. Thereafter, since the aim was to produce a map of benthic habitats, the land area and other adverse objects (such as boats and jetties) were manually masked from the image using an on-screen digitizing method. Before performing image classification, minimizing the water column effects on the reflectance values of benthic habitats was necessary. For this purpose, Purkis and Pasterkamp (2004) formulated the relationship between remotely sensed reflectance values (R_rs) and bottom reflectance values (R_b) (Eq. 1).

Table 1

Specification of WV2 satellite image (Multispectral bands) used in this study

(source: www.maxar.com)
Spacecraft	WV2
Catalog ID	10300100AE77A700
Acquisition Date:	Oct 23, 2020
Acquisition Time (GMT):	06:57:53
Acquisition Time (Local):	10:27:53
Cloud coverage	0.0%
Spatial resolution at nadir	1.8 m GSD (2.4 m at 20º off-nadir)
Spectral bands: wavelength (nm)	*B1-Coastal blue: 400–450
	*B2-Blue: 450–510
	*B3-Green: 510–580
	*B4-Yellow: 585–625
	*B5-Red: 630–690
	B6-Red edge: 705–745
	B7-NIR1: 770–895
	B8-NIR2: 860–1040
* Utilized bands in this research

$${R}_{b}= \frac{\frac{1}{0.54}{R}_{rs}\left(z=a\right)- \left(1-{e}^{-2kz}\right){R}_{w}}{{e}^{-2kz}}$$

In Eq. 1, k refers to the diffuse attenuation coefficient, z is the depth value, and R_w is the mean of reflectance values for an optically deep area in the satellite image. For this study, z values were extracted from the bathymetric data mentioned earlier, and the depth values were corrected in advance using tidal information at the time of image acquisition. The method proposed by Mumby and Edwards (2000) was used to determine k values for five bands (B1 – B5). Consequently, the k value for the i^th band can be determined using Eq. 2.

$${k}_{i}=\frac{0.5\text{ln}\left(\frac{{L}_{i max}-{L}_{i \infty mean}}{{L}_{i min}-{L}_{i \infty mean}}\right)}{{Z}_{i}-{Z}_{j}}$$

In Eq. 2, L_{i ∞ mean} is the average reflectance value for the i^th band over deep water, z_i, and z_j are the maximum depths of penetration for the i^th and j^th zones (where j = i + 1), and Li max and Li min are the maximum and minimum reflectance values for the i^th band, respectively. Table 2 summarizes the determined parameters for the five bands of the utilized WV2 image. Subsequently, the R_b values for all five bands were calculated by replacing these values (together with the extracted z values from the bathymetric map) in Eq. 1. Note that B6 – B8 (the red edge band and the NIR bands) were excluded from the analysis due to the low penetration ability of light in the water body within these wavelengths. Afterward, the masked water area of the corrected satellite image was classified based on the training data obtained from the field measurements and ancillary data mentioned earlier.

Table 2

Summary of calculated L, z, and k_d values for five bands of utilized WV2 satellite image in this study
	L_{i ∞ mean}	L_{i min}	L_{i max}	z_i	z_j	k
Band	(×10²)			(m)		(m^− 1)
B1-Coastal blue	0.2	1	4	3	1	0.389
B2-Blue	4.8	5.8	13	11	3	0.131
B3-Green	3.2	6.3	15	17	11	0.111
B4-Yellow	1.6	4	11	4	1.6	0.284
B5-Red	0.8	1.8	5	2	0.5	0.478

2.3. The Fuzzy-based analysis

Fuzzy overlay analysis for site selection is a decision-making technique used to evaluate the suitability of different locations for a particular land use or development project (Saadat Foomani and Malekmohammadi, 2020; Hammond et al., 2021). It is based on the concept of fuzzy logic, which allows for the representation of partial truths and the handling of uncertainty and ambiguity in decision-making (Nazari et al., 2012). In fuzzy overlay analysis, the suitability of each site is assessed based on a set of criteria, such as environmental, social, economic, or technical factors. Each criterion is assigned a weight that reflects its relative importance in the decision-making process. A membership function is then used to quantify the degree to which each site meets the criteria, based on a set of linguistic variables that describe the level of suitability (e.g., very high, high, moderate, low, and very low). The fuzzy overlay analysis combines the different criteria and their weights through a mathematical operation called overlaying, which results in a suitability map. This method is useful for site selection processes where the criteria are subjective or qualitative, and where there is a high degree of uncertainty or ambiguity (Kharat et al., 2016). In this regard, the fuzzy-based overlay method was applied in this study to combine the criteria for producing a final suitability map, which has shown its higher ability for similar site selection studies (Akbari et al., 2008; Ali et al., 2020).

3.1. Data preparation results

The output of in situ measurements and remotely sensed data processing, as well as the collection of existing ancillary data, was a sort of GIS-based maps that were further used as required input for further analysis. Figure 8 presents the maps produced from SDD observations and CTD measurements, including SDD, which represents water clarity/turbidity (Fig. 8a), Chl-a (Fig. 8b), pH (Fig. 8c), salinity (Fig. 8d), DO (Fig. 8e), and water temperature (Fig. 8f). Water temperature, Chl-a, pH, salinity, and DO were computed by averaging the values recorded by the CTD in the water column from the sea surface to the bottom.

Figure 8.

Figure 9 depicts the classified maps produced by WV2 satellite image processing. The accuracy assessments of these maps show an overall accuracy of ~ 78%, which is acceptable compared to similar studies (Mishra et al., 2007; Reshitnyk et al., 2014; Kabiri et al., 2018). As seen in this figure, the map is generated in six classes, but the first three classes, including coral reefs, rock, and sand, are required for this study to conduct further procedures. Knowing the location of existing corals is vital to avoid constructing AR in their adjacent areas and to extract areas where their larvae can travel based on wave direction. The rock and sand classes will help to obtain information about the seabed resistance for the placement of AR.

Figure 9.

These maps and data should be prepared and rearranged in advance before being employed in further fuzzy-overlay procedures. The reordering of data in this study has been conducted based on criteria that may affect the site selection process for AR. Table 3 summarizes the criteria and their importance scores and weights. Figure 10, represents GIS-based criteria maps that are prepared to be applied in further analysis. These maps are prepared based on the definitions of each criterion presented in Table 3. In the following, the fuzzy overlay tool in ArcGIS software was utilized to produce a suitability map based on these GIS-based data and their importance weights. In doing so, the normalized importance weights (NIW) of each above-mentioned criterion were determined in advance leading to normalized maps with values between 0 and 1. This has done using Eq. 3 as a Min-Max normalization method, which scales the values of each map to the minimum and maximum values of that map.

$$N{IW}_{i}^{j}=\frac{{Score}_{i}^{j}}{{Max}_{score}{Max}_{weight}}$$

Table 3.

Table 3

The twelve defined criteria and their relevant values, scores, weights, and determined NIW values
No.	Criterion	Value	Score	Weight	Normalized importance weight (NIW)
1	Distance with existing corals (m)	< 200	3	3	0.5625
		200–500	2		0.3750
		> 500	1		0.1875
2	Site accessibility by boat regarding to the distance with diving clubs (m)	< 500	4	4	1.0000
		500–1000	3		0.7500
		1000–1500	2		0.5000
		> 1500	1		0.2500
3	Site accessibility by swimming regarding to the distance of AR with coast (m)	< 200	4	2	0.5000
		200–500	3		0.3750
		500–1000	2		0.2500
		> 1000	1		0.1250
4	Water depth (m)	< 5	3	3	0.5625
		5–10	4		0.7500
		10–15	2		0.3750
		> 15	1		0.1875
5	Water clarity/transparency, SDD (m)	< 5	1	3	0.1875
		5–7	2		0.3750
		> 7	3		0.5625
6	Slope (%)	< 0.5	4	2	0.5000
		0.5–1.0	3		0.3750
		1.0–2.0	2		0.2500
		> 2	1		0.1250
7	Substrate type	Mud and unstable sand	1	2	0.1250
		Soft sand	2		0.2500
		Hard sand	3		0.3750
		Rock	4		0.5000
8	Chlorophyll-a (mg/m³)	< 0.5	4	2	0.5000
		0.5–1.0	3		0.3750
		1.0–5.0	2		0.2500
		> 5.0	1		0.1250
9	Current orientation (in the path of currents passing through nearby natural coral reefs)	Very high	4	2	0.5000
		High	3		0.3750
		Low	2		0.2500
		Very low	1		0.1250
10	Current speed (knots)	< 1	4	3	0.7500
		1–2	3		0.5625
		2–3	2		0.3750
		> 3	1		0.1875
11	The percentage of coverage and species diversity of corals and the diversity and abundance of fishes in nearby natural coral patches	Very high	4	4	1.0000
		High	3		0.7500
		Low	2		0.5000
		Very low	1		0.2500
12	The status of species diversity of benthic organisms (the epibenthic substrate) in the candidate site	Very high	4	3	0.7500
		High	3		0.5625
		Low	2		0.3750
		Very low	1		0.1875

Figure 10.

In this equation, i represents the criterion number in Table 3 (ranging from 1 to 12), while j represents the score number of the i^th criterion (ranging from 1 to 3 or 4). The Max_score and Max_weight values denote the maximum scores and weights, respectively, which are set to 4 in this study, as shown in Table 3. Figure 11 illustrates the process flow of converting GIS-based maps to NIW maps followed by utilizing them in a fuzzy-based analysis for site selection procedure.

Figure 11.

3.2. Land suitability and fuzzy-based analysis outputs

After the preparation of NIW maps, the fuzzy-based overlay module in ArcGIS software was utilized to produce a final suitability map for the site selection of AR. Figure 12 depicts the final suitability map produced based on this process. As illustrated in Fig. 12, a few locations have been categorized as very highly suitable for deploying AR around KI. Most of these locations were in the southeastern areas where the main coral reefs and diving clubs were located, and where the distance from diving clubs was a key factor in this study.

To evaluate the accuracy and reliability of the map produced through the proposed method in this study (Fig. 12), we examined the outcomes of previous projects involving AR implementation around KI. For instance, a past project deployed AR in the northern region of KI approximately 10 years ago, but over time, these installations gradually sank. This failure stemmed from disregarding assessments of the seabed type and its durability. Another unsuccessful endeavor involved deploying AR in an area with a depth of 18 meters, far from diving stations. Consequently, non-professional divers couldn't access these installations and diving clubs preferred to bring the customers to natural and closer reef sites, consequently, no tangible benefits accrued toward reducing anthropogenic stressors on the endangered coral reefs of KI. Both of these sites are classified as low and very low suitable in the map depicted on Fig. 12.

On the other hand, the results of the present study are quite comparable with a similar study by Mousavi et al. (2015) in KI who considered relatively different sets of criteria grouped into three categories including (1) physical characteristics of the sea consisting of water depth, water temperature, salinity, turbidity, marine currents, bottom slope, and bottom type, (2) biological factors consisting of larval dispersal, distance from natural reefs, distance from competitive species, and (3) managerial factors including distance from pollutant sources, and distance from ship waterways. They further ignored the distance from diving clubs, and accordingly, their results were different from the results of the present study. In the study by Mousavi et al. (2015) the sites with very high suitability for AR sitting in KI were in the north, northwest, and south of KI, whereas, in the present study the sites with very high suitability were in the east, south, and west of KI. Differences in the outcomes of these studies probably reflect differences in some issues such as methods of site selection, the criteria for site selection, the range set for a given criterion, and the objectives for AR site selection.

The present study used Fuzzy-based analysis, whereas Mousavi et al. (2015) employed a Multi-Criteria Decision-Making (SMCDM) tool that was based on the combination of existing Multi-Criteria Decision-Making (MCDM) with Expert Systems (ES), Weighted Linear Combination (WLC) and field study. In this study, many environmental factors that can impact the success of AR deployment have been taken into account, but the precise effects of these factors may be uncertain or difficult to quantify. The utilized fuzzy-based method provided a way to represent and reason with imprecise or uncertain data by using linguistic variables, allowing them to consider the uncertainty inherent in the complex and dynamic nature of marine systems. By using fuzzy-based analysis to evaluate the impact of various factors on the success of AR deployment, the algorithm can provide more nuanced and flexible recommendations for site selection that account for the complex and uncertain interactions among environmental factors. On the other hand, the main objective of site selection for AR in the present study was to select suitable sites for deploying AR that are set to function as surrogates for natural reefs to attract SCUBA diving and lower the pressure driven from diving on natural reefs. In contrast, in Mousavi et al. (2015), the main aim of site selection for AR was to select the most suitable areas where the deployed AR can be colonized by corals by attracting the coral larvae driven from natural reefs.

Figure 12.

Fuzzy-based analysis as a type of mathematical logic deals with reasoning that is approximate rather than fixed and exact. It is particularly useful when dealing with complex systems that involve uncertain or ambiguous information, such as environmental systems. In the case of deploying AR around KI, a fuzzy-based method could be used to evaluate various factors (e.g., water temperature, water depth, currents, the presence of natural reefs, or other marine life) that could affect the success of the AR. These factors could be assigned as fuzzy sets, which would allow for a more flexible and nuanced analysis of their impact on the suitability of different locations for AR. Once the fuzzy sets have been established, a fuzzy inference system could be used to determine the best locations for AR based on the input data. The system would use a set of rules to evaluate the fuzzy sets and make decisions based on the degree of membership of each factor in the fuzzy sets. The output of the system would be a set of recommended locations for deploying AR, along with a degree of confidence in each recommendation. Overall, utilizing a fuzzy-based method for allocating the best places for deploying AR around KI could provide a more comprehensive and accurate analysis of the complex factors that affect reef success. It could also lead to more effective and sustainable management of marine resources in the PG.

Ethical Approval

Not applicable

Funding

This research was supported by Iranian National Institute for Oceanography and Atmospheric Science [grant no. INIOAS-1401-023-01-012-01], and also Iran National Science Foundation [grant no. INSF-99031644]

Availability of data and materials

Data are available in request

Author Contribution

K.K. initialised the methodology and wrote the main MSM. M. and H. R. B. provided field measurementM. R. S. reviewed the MS.

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No competing interests reported.

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A Fuzzy-based method for artificial reefs site selection- Case Study: Kish Island, the Persian Gulf

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1. Study area

2.2. Types of data and information

2.2.1. Collecting the existing maps and data

2.2.2. Field observations

2.2.2.1. Underwater SCUBA diving photography

2.2.2.2. Snorkeling photography

2.2.2.3. CTD measurements and SDD observations

2.2.3. Remotely sensed data

2.3. The Fuzzy-based analysis

3. Results

3.1. Data preparation results

3.2. Land suitability and fuzzy-based analysis outputs

4. Conclusion and Summary

Declarations

References

Additional Declarations

Status:

Version 1