Fire detection in Kambalakonda Reserved Forest, Visakhapatnam, Andhra Pradesh, India: An Internet of Things Approach

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1 Available online at ScienceDirect Materials Today: Proceedings XX (2016) XXX XXX PMME 2016 Fire detection in Kambalakonda Reserved Forest, Visakhapatnam, Andhra Pradesh, India: An Internet of Things Approach Gudikandhula Narasimha Rao a *, Peddada Jagadeeswara Rao b, Rajesh Duvvuru c, Sridhar Bendalam d, Roba Gemechu e a,b,c,d,e Department of Geo-Engineering & Centre for Remote Sensing, Andhra University, Visakhapatnam, Andhra Pradesh, India. *Corresponding author narasimha.geo@cea.auvsp.edu.in Abstract False alarms are in terms of fire safety engineering fire alarms in absence of areal fire condition. They are related to the exchange of information and occur as a side effect of system technologies and the alerting process. Fires often are associated with considerable damages. Because time is a critical factor, detection is often accomplished by installed Spatio Wildfire Prediction and Monitoring System (SWPMS). The highly sensitive sensors in fire detectors make the identification of fires in a nearly stage possible, but this also makes SWPMS susceptible to false alarms. So far there is no reliable evidence on the ratio of false alarms to installed fire detection and fire alarm systems in SWPMS. The studies, which are described in the present paper, close this gap for Germany on the basis of an empirical analysis of data from fire brigades. It is difficult to describe the ratio in one single value, because a great number of conditions and influencing factors are relevant. To reduce the false alarm ratio, we need a common understanding of false alarms and a consistent use of terms and categories Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Processing of Materials, Minerals and Energy (July 29th 30th) 2016, Ongole, Andhra Pradesh, India. Keywords: False alarm ratio, Global Positioning System, Geographic Information System, Internet of Things. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. * Corresponding author. Tel.: address: narasimha.geo@cea.auvsp.edu.in Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of International Conference on Processing of Materials, Minerals and Energy (July 29th 30th) 2016, Ongole, Andhra Pradesh, India.

2 2 Gudikandhula Narasimha Rao/ Materials Today: Proceedings XX (2016) XXX XXX 1. INTRODUCTION In this study, the problem of video based flame detection techniques has been deciphered. The important challenge in video-based flame detection describes chaotic and complex nature of the fire phenomenon. Most of the fire flames consists large variations in particular place of fired regions [1]. To address this problem, motion characteristics of flame and spatial distribution of fire colours will be combined to arrive temporal and spatial characteristics in accordance of the scene. Though, several natural objects like sun light, artificial lights have similar colour of fire behaviour. Mistakenly, the human eye and sensors may be treated as those regions are fired regions. Additionally low video resolution should be affected on the exact fire regions and flames. Automatically the robustness of computer vision based fire detection algorithms will be decrease and then the false alarm rate will be increase [2]. A naïve technique is presented in this study for fire detection and tracking based on kalman filter object tracking algorithm. It describes spatiotemporal flame modelling and dynamic object detection analysis. It reduces computational cost to eliminate redundant data through dynamic textural analysis. It finds out fire incident timings dynamically, localizes exact point of fire in the image [4]. The flame behaviour modelling is a method to observed texture of fire, the movements of flame and random variations of its appearance. A dynamic approach is used to finds out reliability of the algorithm by using kalman filter. Firstly, different features retrieved from both dynamic texture analysis and Spatio-temporal flame modelling. Secondly, comparing between previous fired regions with current fires based on neighbouring blocks. Fire information will be transfer to Central system, that data will be classified by classification algorithm [6]. The final data reports of fire will be possessed on semantic web3.0 (Internet of Things). Geographic information system for quantitative evaluation of fired regions due to ground forest damaged information by using ArcGIS software. Fire information automatically sent to nearest peoples, civilians and National Disaster Response Force (NDRF). Previous research may not provide any rescue system for forest fires. So alerts to be sent to the surrounding peoples as well as rescue forces. Therefore, main theme of the research is to maintain dynamic fire alerting system. It describes more features for prevention and detection of forest fires. The ecological procedure 2008 describes forest fire is biggest disaster in the world because they will increase the ecological problems due to reason of large amounts of green house gases are releasing into the environment [3]. This paper is described as below partitions. Study area is discussed in detail in second section. Third section describes proposed architecture for SWPMS, location detection algorithm with alert sent to surrounding peoples. Section 5 fire flame detection algorithm, results and discussion. Sections 6 and 7 mention has been made in conclusions and references. 2. MATERIALS AND METHODS 2.1 Study Area The Kambalakonda Reserve Forest spread over 7,146 hectares located near Visakhapatnam, Andhra Pradesh, India. The forest covers under Eastern Ghats has rare species of endemic flora and also live in by wild animals such as leopard, barking deer, wild boat, spotted deer, sambar and monkeys. It is close proximity to the city and high degree of biotic interference. The sanctuary is adjusted between latitudes of N to N and longitudes of E to E. That place corresponds to an area west of National Highway 5 on the northern side of Visakhapatnam and Pendurthi in Vizianagaram district. In Visakhapatnam region Simhachalam hills, Kailasapuram hills consists number of fires frequently from last several years. In the year of 2015, Simhachalam hills have biggest fire in March. Kailasapuram hill also consist a huge fire in December, So this research fully concentrated on Visakhapatnam region forest fires. In the year 2015, Kamabalakonda reserve forest has number of fire occurrences are there due to reason of environment conditions and man made mistakes. Generally, forest fires are mostly occurs from February to May. It has 8 times forest fires in February, 34 times March, 5 times in April, 5 times in May. Therefore based on temperature conditions most probable forest fire was done in the month of March. In the month of November, 2014, the Kamabalakonda forest fires were done due the reason of manmade mistake on the day of Kartheeka Pournami. That was a biggest disaster in Visakhapatnam, participated naval helicopters, fire engines and NDRF.

3 Gudikandhula Narasimha Rao/ Materials Today: Proceedings XX (2016) XXX XXX 3 (a) (b) Figure.1. (a) Location Map of Kambalakonda Reserved Forest, Visakhapatnam, Andhra Pradesh, (b). Total forest fires in the month of May from 2013 to 2015 in Andhra Pradesh, India. Figure. (b) describes last three years forest fired information in Andhra Pradesh ( ). When comparing to all districts the Visakhapatnam district has high fired rates, those are especially occurring in Kambalakonda Reserved Forest, Simhachalam hills and Kailasapuram hills. That fire rate mostly occurred in the month of May. 2.2 Proposed Architecture for Spatio Wildfire Prediction and Monitoring System (SWPMS) The architecture describes wireless sensor network in forest areas. The system has different antennas, fire watch tower, space borne and centralized server with fire classifier algorithm. The classification of fire flames will be sent to semantic web3.0 with dynamic updates of fire detections. The texture analysis algorithm identified natural objects as fired objects, then that situation every object should be compared to past data. So careful selection of Spatio temporal features are required for modelling of fire behaviour. The SWPMS consists of a mobile application development for Android smart phones as a client. This device may communicate with centralized server through internet connection. The system has some functionality for alerting of persons at surrounding places of forest regions. i) Always reporting fire accidents at any time and any movement ii) Display the total vegetation of an area iii) Based on past information organizing trained scenarios, i.e., fire simulations and detailed safe route maps. ArcGIS provides such type of detailed forest fire thematic information on the map. In Figure.1.(a) demonstrates Location Map of Kambalakonda Reserved Forest, Visakhapatnam, Andhra Pradesh, India. The mobile device users are enabling to access with the central system by sending http requests and retrieving information from their mobiles. The entire process should be depending on SWPMS functionalities and mappings. Whenever the client is able to report fire images to central system then automatically the system will be identified mobile device information like position and timings of fires. Simultaneously, a message or electronic mail is will be send to rescue persons or NDRF team. The system may consist of low power sensors [4]. The natural phenomenon is observed by temperature sensor; whenever forest fires occur then automatically temperature will increase. If forest fire is increasing then weather conditions also may vary, generally such type of changes will be placed at summer season. Finally number of trees, plants and bushes are burnt, automatically those are distributing more energy than the temperature will be increased and finally entire vegetation will be spoiled. If the air of fire will be dry then the humidity sensor identified such type of features, finally the humidity will be decreased. In some of the situations the high levels of the humidity can received by the smoke sensor then false alarm rates may be increased. Gazes and Smoke sensor identifies appearance of smoke in forest fires, weather smokes. These sensors are very small in size and are placed in within the fencing [5].

4 4 Gudikandhula Narasimha Rao/ Materials Today: Proceedings XX (2016) XXX XXX SWPMS describes required specifications or rules for designing of architecture. It has following constraints. Step 1. Identify fire flames within a short and reasonable time. i.e., fast accessing and immediate response. Step 2.Retrieved data should be sent to central server with reliable or exact manner. Step 3. It reports all weather conditions to server. Because the system will be identifying that is exact fire or not. i.e., robustness of the system [11]. Step 4. It could be consumes a very minimal energy. Here we are using low power sensors with high accuracy and efficiency. Step 5. Every sensor should have recharged battery by using solar energy. Therefore this system is eco friendly. Step 6. WLAN IEEE protocols are using in transmitting data from thermal sensors to access point as well as access point to central server. Step 7. The classifier should analyse whether that is exact fire or non fire. Step 8. If it is the fire then alert message will be sent to surrounding places of fire regions and National Disaster Response Force (NDRF) team. Step 9. The total fire information should posses to semantic web 3.0. Every person in the world may analyse the forest fire regions without any physical contact. Step 10. Geo spatial semantic web may analyse past information of forest fire, present fires. Based on past information it will be predicting future forest fires by using Geographic Information system (extreme weather, wind, etc.). Generally, fired objects may consist of high spatial variation than fire coloured objects. Spatial variation techniques to be used in that fired regions. Here edge detectors and interest a point descriptors are using to find spatial variation and resolution in vast field. The highest computational efficiency should be takes place whenever we can use simple filters in wavelet analysis. Candidate block is a fire region that compares with spatial wavelet analysis. Every flame image should be classified by k-means algorithm. The support vector machine describes that area has fire or not [6]. 3. LOCATION DETECTION ALGORITHM At present, there are many ways to accurately position the location of information, for example, Global Positioning System (GPS) positioning. But the user nodes are usually of high energy consumption, large volume, high cost, and it also need a fixed infrastructure. Therefore, in the case of GPS applications are limited, sensor networks with small size, low power consumption, low-cost sensor networks can well solve the positioning problems. The typical wireless sensor network positioning technology consist of methods based on the received signal strength indication (RSSI), time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA). Y it {O, 1} represents the detected value of the node i at time t, to detect the target value is 1, not to detect the target value is 0. Therefore, Y t = (Y 1t,..., Y nt ) means all the detection results of all sense nodes at time t. Y t = ( Y 1, ---, Y T ) is the result of all the detection before time t. X 1, T = (X 1,., X T ) is the state set of targets before time t. According to the description above, the joint distribution function can be drawn: P(X,Y)=P(X) π t=2 P(X t X t-1 ) π t = 2 P(Y t X t ) (1) Maximum possibility at time t= (1.T), i.e., max P(X 1,.,X t-1,x=i,y 1,t ) (2) At time t, coordinates with zeros value greater than 0 are as follows: (x 1, y 1 ), (x 2, y 2 ),, (x n,y n ) and Weighted value of each corresponding sensor nodes are: With the weighted value, the coordinate of the target can be drawn: X= n i=1 WiXi, Y= n i=1wiyi (3)

5 Gudikandhula Narasimha Rao/ Materials Today: Proceedings XX (2016) XXX XXX 5 By the algorithm description above, this kind of node weights can be deduced according to the sensing area of sensor nodes, the final location of each sensor nodes are approximately located. The background modelling is estimation of fire flames by using subtraction algorithm. The main theme of this one is to convert the video frame in to binary classification by using mat lab soft ware. Pre-processing is applying on forest fired field; it covers all pixels in static view as well as classifies the reaming fired regions based on object tracking or flame detection. It shows frame to frame variation in simple manner since identification of back ground subtraction. This method finds the accurate pixel to pixel correlation as well as frame to frame correlation. It takes adjacent pixels of image as the background image of present fire fields and find out changes of all image sequences based on frame to frame variation. Finally it is followed by filtering techniques. It has high speed and is suitable for different applications in real time. 4. RESULTS AND DISCUSSIONS The mobile application environment allows to any people can implement system techniques. Firstly, reporting the fire incidents to Spatio Wildfire Prediction and Monitoring System (SWPMS) by sending information through . The SWPMS identifies fired image, stores location coordinates of fire, rotation angles of smart phone and location timings. The basic knowledge of fired region is depending upon the area latitude and longitude. Therefore, system must be careful with that coordinates and finally conclude the fired regions. Secondly, the SWPMS describes list out types of vegetation in forest and identifies which vegetation is caused to more fires. Table 1: Region- and district-wise forest burnt area statistics of Andhra Pradesh: District Geographical Forest cover Forest burnt % of burnt % of state forest area (km 2 ) (km 2 ) area (km 2 ) area/forest cover burnt area (A) Rayalaseema Kurnool Cuddapah Ananthapur Chittor Subtotal (B) Coastal Andhra Visakhapatnam Prakasham East Godavari West Godavari Vizianagaram Srikakulam Krishna Nellore Guntur Subtotal Grand total The Geographic Information System is used to find out destroyed vegetation map as well as fire assessment. The probability theory measures total forest fire lands [7]. The SWPMS consist fire simulation soft ware and the algorithm estimates fire flames propagation paths. The simulation algorithm mainly depending on different parameters like wind speed, wind direction, humidity, terrain aspect, terrain slope, fuel models, ignition points and other parameters. Such parameters will be provided by any person or the algorithm by default due to present fire features.

6 6 Gudikandhula Narasimha Rao/ Materials Today: Proceedings XX (2016) XXX XXX Table 2: Areal extent of forest burnt area across elevation zones. Sl. No Elevation Forest area Forest burnt % of burnt Zone (m) (km2) area (km2) area 1 < Grand total The SWPMS should be supported to Arc Geographic Information System software. It has different plug-in, easy to user window with good analysis tools. The system shows all received information on the map. It has high capable to evaluating the probability of fire and classification of fire images. Mainly the system should be plays a vital role in forest fires [8]. The state of Andhra Pradesh is one of the biggest fire prone state in India. The forest fires in Andhra Pradesh are frequently ground fires because dry leafs, garbage. Whenever surface fire will be increase then automatically ground fires also increased, those have good association. The spatial analysis of fire occurrence and destroyed lands information in 2009 was described below. The Andhra Pradesh northern parts will be biggest threat due to burn agriculture [9]. Where Visakhapatnam has 23.3% forest burnt area. The object track function calling to Kalman filter algorithm, it should be plots a curve of any object in blue texture. Then the algorithm will be estimated position in green colour [10]. It is a very little time to take evolution of position of an object. Finally three rotating steps will be occurred. This is a continuous process to trace an object in little iteration. This algorithm applied on two types of fire regions in Visakhapatnam region, those are kailasapuram and simhachalam hills. It shows chronological object tracking in different frames. Initially the tracking of first frame is low fire rate that is low object detection; slowly it should increase up to fast object detection in exact manner [12]. Table 3: Size class distribution of forest burnt areas in Andhra Pradesh. Sl. No Burnt area Number of Area % of % of burnt Patch size (ha.) patches (km 2 ) patches area 1 < > Grand total In Kalman filter, spatial texture conservation of fire regions are divided in to three clusters including the RGB,YCbCr, HSV and so on. Here the fire region will be converted to other colour spaces, where RGB is the basic colour. The fire region of HSV describes brightness of the fire [13]. Total number of burnt land patches was described to be Different categories sizes of burnt lands analysed in Table 3. That categorization refers patch analysis of fired lands information; those are all less than 25 ha. The burnt land is greater than 400 shows sq. km [14].

7 Gudikandhula Narasimha Rao/ Materials Today: Proceedings XX (2016) XXX XXX 7 5. CONCLUSIONS Fire detection and fire alarm systems are useful means to reach the objectives of fire protection engineering. The early alert can reduce property damage considerably, and even more importantly, save human lives. False alarms are a side effect of these systems. False alarms are combined with economical costs and acceptance losses; in some cases, with an increased traffic risk of the rescue forces. In the worst case, a fire alarm is not taken seriously. On the other hand, false alarms can function like a fire drill, providing an opportunity to practice safe escape. This may prevent undesired stress reactions. The problem of false alarms is not new. Manufacturers, constructors, planners, certifiers of installed FDAS have for many years taken remedial measures to reduce the ratio of false alarms particularly in introduction of the multi-sensor fire detectors. ACKNOWLEDGEMENTS Authors thank to forest conservative officer, Visakhapatnam, India for their data support from last several years. Special thanks to ABN Andhra Jyothi, Visakhapatnam, India for supply of fire videos to this study. REFERENCES [1] Gudikandhula Narasimha Rao, et.al, An Enhanced Real-time Forest Fire Assessment Algorithm Based On Video by Using Texture Analysis, Perspetives in Science, Elsevier,2016. [2] Gudikandhula Narasimha Rao, et.al, A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach IOP Conference Series: Materials Science and Engineering, ISSN X, [3] Chen T H, Wu P H, and Chiou Y C, An early fire-detection method based on image processing, Image Processing, ICIP' International Conference on. IEEE, 2004, 3: [4] B. Toreyin, Y. Dedeoglu, U. Gudukbay, and A. Cetin, Computer vision based method for real-time fire and flame detection, Pattern Recognit. Lett., vol. 27, no. 1, pp , Jan [5] A. Elgammal, D. Harwood, and L. Davis, Non-parametric model for background subtraction, in Proc. 6th Eur. Conf. Comput. Vis., Dublin, Ireland, Jun. 2000, pp [6] C.-B. Liu and N. Ahuja, Vision based fire detection, in Proc. ICPR, Aug. 2004, pp [7] Eastman, J.R., Zhu, H., Lazar, A., Williams, D.W., Progress on remote sensing applications for emerald ash borer survey: analysis of 2004 hyperspectral imagery. Emerald Ash Borer Research and Technology Development Meeting. [8] Fassnacht, F.E., Latifi, H., Ghosh, A., Joshi, P.K., Koch, B., Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sens. Environ. 140, [9] K. Dimitropoulos et al., Flame detection for video-based early fire warning for the protection of cultural heritage, in Proc. 4th Int. Conf. Prog. Cult. Heritage (EuroMed), Limassol, Cyprus, Oct. 2012, pp [10] G. Doretto, A. Chiuso, Y. Wu, and S. Soatto, Dynamic textures, Int. J. Comput. Vis., vol. 51, no. 2, pp , Feb [11] Rajesh Duvvuru, et.al, "Scheme for Assigning Security Automatically for Real-Time Wireless Nodes via ARSA". Title: Heterogeneous Networking for Quality, Reliability, Security and Robustness, QSHINE 2013, SPRINGER, LNICST 115, pp , Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN [12] Rao, Gudikandhula Narasimha, and P. Jagdeeswar Rao. "A Clustering Analysis for Heart Failure Alert System Using RFID and GPS." ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol I. Springer International Publishing, [13] Z. Zhang, J. Zhao, D. Zhang, C. Qu, Y. Ke, and B. Cai, Contour based forest fire detection using FFT and wavelet, in Proc. CSSE, Dec. 2008, pp [14] Yang J, Chen J Z, A New Image Based Smoke Detection Algorithm, Journal of Southwest University of Science and Technology, 2011, 26(3):