ประมวลบทควำมกำรประชุมวิชำกำรวิทยำศำสตร์ทำงทะเล ครั้งที่ 5 วันที่ 1-3 มิถุนำยน พ.ศ.2559 โรงแรมรำมำกำร์เด้นส์ กรุงเทพฯ O-F-011 ระบบแสดงผลสภาพอากาศที่อ่าวบ านดอนเพื่อใช เป็นแนวทางบริหารจัดการความเสี่ยงการเลี้ยงหอยนางรมุ อ่าวบ านดอนุจังหวัดส ราษฎร ธานี WeatherุvisualisationุsystemุforุeffectiveุriskุmanagementsุonุoysterุfarmingุatุBandonุ Bay,ุSuratุThaniุProvince กฤษณะเดชุเจริญส ธาสินี*ุุมัลลิกำ เจริญสุธำสินี พีรวิชญ์ เควด และศิริลักษณ์ ชุมเขียว KrisanadejุJaroensutasinee*, Mullica Jaroensutasinee, Peerawit Koad, and Sirilak Chumkiew ศูนย์ควำมรู้เฉพำะด้ำนนิเวศวิทยำพยำกรณ์และกำรจัดกำร ส ำนักวิชำวิทยำศำสตร์ มหำวิทยำลัยวลัยลักษณ์ CenterุofุExcellenceุofุEcoinformatics,ุSchoolุofุScience,ุWalailakุUniversity *ุCorrespondingุauthor’sุe-mail: [email protected] บทคัดย่อ ผู้ประกอบกำรฟำร์มเลี้ยงหอยในอ่ำวบ้ำนดอน จังหวัดสุรำษฎร์ธำนีมักประสบคล ่นลมแรงในช่วงเด อน พฤศจิกำยน-เด อนมกรำคมของทุกปี สร้ำงควำมเสียหำยกับหอยนำงรม หอยแครงและหอยแมลงภู่ที่เพำะเลี้ยง วัตถุประสงค์ของกำรศ กษำครั้งนี้ค อกำรพัฒนำระบบติดตำมและเต อนภัยที่แสดงผลแบบทันทีออนไลน์ส ำหรับฟำร์ม หอยนำงรม ระบบนี้ประกอบด้วยสถำนีวัดอำกำศอัตโนมัติและสถำนีวัดอุณหภูมิน้ ำอัตโนมัติที่มีหัววัด 4 หัวและระบบ กล้องวิดีโอที่มีกล้องวิดีโอไร้สำย 4 กล้อง ผลกำรศ กษำพบว่ำ ระบบดังกล่ำวท ำงำนได้ดีตั้งแต่ปี 2014 อ่ำวบ้ำนดอน ได้รับคล ่นลมแรงในฤดูมรสุมในเด อนธันวำคม 2014 อุณหภูมิของน้ ำลดลงอย่ำงรวดเร็วจำก 32 C ในช่วงกลำงเด อน พฤศจิกำยน ถ ง 24 C ในช่วงปลำยเด อนธันวำคม 2014 อุณหภูมิของน้ ำลดลงได้เกิดข ้นอีกในเด อนธันวำคม 2015 จำก 32 C ลดลงเหล อ 28 C ภำยในเวลำ 2-3 วันโดยไม่มีคล ่นลมแรง ข้อมูลลักษณะดังกล่ำวส ำคัญมำกส ำหรับใช้เป็น ข้อก ำหนดในกำรพัฒนำระบบเต อนภัยที่อ่ำวบ้ำนดอน ABSTRACT Oyster farmers at Bandon Bay usually face strong wind and wave during November–January every year causing a major loss in oysters, blood cockles and green mussels. The main objective of this study was to develop the monitoring and disaster warning system near-realtime online for oyster farming. This system was composed of an automatic weather station, an automatic water temperature station with four temperature sensors, and an ecocam system with four wireless cameras. Our results showed that this system worked well since 2014. Bandon Bay faced strong wave and wind during a monsoon season in December 2014. During strong storm surge events, water temperature data showed a drastically decrease from 32 C in the middle of November to 24 C in late December 2014. Similar trends of temperature declines in 2015 also were observed (water temperature decreased from 32 C to 28 C) within a few days with no strong wave and wind. These kinds of data would be essential to be used as a criterion for developing a disaster warning system at Bandon Bay. Keywords:ุdisaster warning, oyster farm, sensor network, risk management, Bandon Bay, Thailand 83 Proceedings of the 5th Marine Science Conference 1-3 June 2016 Rama Gardens Hotel, Bangkok Introduction Sensor networks are envisioned to enable application for environmental monitoring (Pompili et al., 2009; Xie et al., 2010). They allow for the monitoring and detection of phenomena more accurately and rapidly in a variety of geographical areas. Recently, applying sensor networks in underwater environments has received growing interest (Akyildiz et al., 2004; Chitre et al., 2008; Liu et al., 2008). Cameras have been extensively used in ecology by taking advantage of the camera's ability to provide unobtrusive observations over long time periods in inaccessible locations (Karanth and Nichols, 1998; Silveira et al., 2003). Most camera deployments are only for short periods limiting the number of images that are captured and the number and type of events recorded. A permanently installed network connected web camera can capture a constant set of images and data indefinitely ensuring that even rare events are sampled. Similarly, networked sensors measuring physical characteristics of ecosystems can provide high-resolution records over long time periods. Integrated sensor suites for capturing numeric and image data can generate high data rates. These high data rates and the heterogeneity of the data types demand new approaches to networking, data management, visualisation, and analysis. Bandon Bay, formerly served as a nursery ground and feeding area for juvenile shellfish of great economic importance (Wattayakorn et al., 1999; Jarernpornnipat et al., 2004; Chumkiew et al., 2015). Nearly 90% shellfish aquacultures in Bandon Bay area were destroyed during the severe flooding event in March 2011 due to freshwater discharges and sediments from Tapi-Pumduang watershed (Chumkiew et al., 2015). The economic loss was estimated to be 800 million Baht. These demonstrated the vulnerability of risk management for the shellfish aquaculture (i.e. oyster, blood cockle and green mussel) in this area. Shellfish aquaculture at Bandon Bay yields billion Baht per year with a total farming area of 80,247 Rai. The severe flooded in March 2011 has been prevented if we have early disaster warning system. At the present, there are no tangible actions to reduce or mitigate the loss if a recurrence in the future. Access to near real-time data during monsoon events, using sensor networks, is essential in advancing our understanding. Early warning of local conditions likely to cause loss of oyster, blood cockle and green mussel production could enhance regional alerts. In this paper, we described a Near-real time weather visualisation system at Bandon Bay, Suratthani, Thailand. MaterialsุandุMethods Studyุsite Bandon Bay is among the most productive coastal areas in southern Thailand (Figure 1). It is located in Surat Thani Province, southern Thailand (Latitude 9º 7′-9º 25′ N and Longitude 99º 9′-99º 39′ E) covering an area of 1215 km2 (Muttitanon and Tripathi, 2005). Bandon Bay is a small open bay with a coastal area of gradual slope and shallow water. A large mudflat extends along the coast to about 2 km from shore contributing to the high sediment rate within the bay (Chuenpagdee et al., 2001). The inner Bandon Bay from Chaiya District to Donsak District covers an area of 480 km2 with 80 km of coastline with an average depth of 2.9 m. 84 ประมวลบทควำมกำรประชุมวิชำกำรวิทยำศำสตร์ทำงทะเล ครั้งที่ 5 วันที่ 1-3 มิถุนำยน พ.ศ.2559 โรงแรมรำมำกำร์เด้นส์ กรุงเทพฯ The climate is characterised by constant high temperature and rainfall. During the period 1983-2012, the average annual rainfall is 1,530.95 mm ranging from 1,025.1 to 2,414.80 mm. The average rainfall in wet and dry seasons is 1,809 mm and 252 mm respectively. Rainfall is highest in November and January is the driest month of the year. The average annual temperature is 26.45 °C with the warmest month in April and the coolest month in December. Bandon Bay is exposed to monsoon weather with northeast winds from November to April, and southwest winds from May to October. In the lower part of the tidal ranges are muddy soils whereas acid sulphate soil is found in the upper part. The major surface freshwater discharge into Bandon Bay is from the Tapi-Phum Duang River watershed with approximately 13,737 million m3 in annual runoff (Bunpapong et al., 1988). Sensors were installed at Sinmana farmstay, Bandon Bay, Surrathani, Thailand (Latitude 9° 14' 17.66" N Longitude 99° 28' 38.78" E) located 3 km away off shore (Figure 1). It takes 30 min long- tailed boat ride from the Thathong River to Sinmana Farmstay. This farmstay has adequate infrastructure for deploying sensors. Figureุ1 Map of Thailand and study site at Sinmana Farmstay, Bandon Bay, Suratthani, Thailand. DataุCollection There were three kinds of sensors installed at Bandon Bay: (1) automatic weather station, (2) automatic water temperature station, and (3) ecocam (Figure 2). One set of automatic weather station was installed at the Sinmana Farmstay since 14st November 2014. Climatic factors were composed of the amount of daily rainfall, max/min temperature, relative humidity, solar radiation, UV index, wind speed and wind direction. Four water temperature sensors were deployed at 0.5, 1.0, 1.5 and 2.0 m water depth. Both weather and water temperature data were reported near realtime online.ุFour wireless cameras were installed since 18th March 2015 to capture images displaying weather conditions at Sinmana Farmstay. This site used a variety of techniques to visualise and share data. Our primary objective was to make information freely and easily accessible both to ecological researchers and oyster farmers. All visualisation in this study was generated by Mathematica software. 85 Proceedings of the 5th Marine Science Conference 1-3 June 2016 Rama Gardens Hotel, Bangkok ResultsุandุDiscussion Since the system becoming operational, the system has provided scientists with significant new insights into shellfish aquaculture at Bandon Bay. Meteorological conditions were collected. Video images were captured (Figure 3). The system has been online since March, 2015. The field data acquisition and near real time online system has been stable. The only interruption in this system was ecocam power outage. Bandon Bay was faced strong wave and wind during a monsoon season in December 2014 when automatic weather station and water temperature station recorded water temperature decreased from 32 C in mid-November 2014 to 24 C in late December 2014 ( Figure 4a). The water temperature data from 2015 also showed the decreasing trend (water temperature about 32 C to 28 C) within a few days with no strong wave and wind. The data would be very essential to be used as a criterion for developing a disaster warning for oyster farming at Bandon