Top

Published in:

28-06-2023 | Technical Paper

Neural fuzzy system design in forest fire detection

Authors: Gudikandhula Narasimha Rao, Peddada Jagadeeswara Rao, Rajesh Duvvuru, Kondapalli Beulah, E. Laxmi Lydia, Prasanthi Rathnala, Bangaru Balakrishna, Vijaya Raju Motru

Published in: Microsystem Technologies | Issue 4/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Forest fire detection using neural networks, remote sensing, and Geographic information systems (GIS) is an effective approach to monitor and respond to forest fires. This integrated system combines the power of machine learning, satellite imagery, and spatial analysis to detect and track fire incidents in real-time and forest fire detection systems can significantly improve early detection, response efficiency, and overall management of forest fires. Element swarm optimized neural fuzzy (ESONF), network learns to recognize patterns and features indicative of fire occurrences in Greater Visakhapatnam Municipal Corporation (GVMC). Element swarm optimization (ESO) is a combined approach to improve the efficiency of fire incidents in real time. This region has most prone area of frequent fires and integrated approach enhances the ability to protect ecosystems, human lives, and infrastructure from the devastating impact of wildfires. It enhances to create an alert to neighbors, surrounded people of the forest and fire fighters team like NDRF and APSDRF.

previous article Fundus vessel structure segmentation based on Bel-Hat transformation

next article MKFF: mid-point K-means based clustering in wireless sensor network for forest fire prediction

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Abdusalomov AB et al (2023) An improved forest fire detection method based on the detectron2 model and a deep learning approach. Sensors 23.3:1512CrossRef

Abedi Gheshlaghi H, Bakhtiar F, Thomas B (2020) GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J Environ Plan Manag 63.3:481–499CrossRef

Amiri T, et al. (2022) Using forest fire experts opinions and GIS/remote sensing techniques in locating forest fire lookout towers. Appl Geom: 1–15

Apostolopoulos ID et al (2022) An explainable deep learning framework for detecting and localising smoke and fire incidents: evaluation of Grad-CAM++ and LIME. Mach Learn Knowl Extract 4.4:1124–1135CrossRef

Ashraf S (2020) Culminate coverage for sensor network through Bodacious-instance Mechanism

Ashraf S et al (2020) Bodacious-instance coverage mechanism for wireless sensor network. Wirel Commun Mob Comput 2020:1–11

Ashraf S, Ahmed T, Saleem S (2021) NRSM: node redeployment shrewd mechanism for wireless sensor network. Iran J Comput Sci 4.3:171–183CrossRef

Baballe MA, Bello MI (2022) Gas leakage detection system with alarming system. Rev Comput Eng Res 9.1:30–43CrossRef

Barmpoutis P et al (2020) A review on early forest fire detection systems using optical remote sensing. Sensors 20.22:6442CrossRef

Bukhari MM et al (2021) An improved artificial neural network model for effective diabetes prediction. Complexity 2021:1–10CrossRef

Dampage U et al (2022) Forest fire detection system using wireless sensor networks and machine learning. Sci Rep 12.1:46CrossRef

Eugenio FC et al (2016) GIS applied to location of fires detection towers in domain area of tropical forest. Sci Total Environ 562(2016):542–549CrossRef

Jin Ri, Lee K-S (2022) Investigation of forest fire characteristics in North Korea using remote sensing data and GIS. Remote Sens 14.22:5836CrossRef

Jovith AA, Rama Sree S, Narasimha Rao G, Vijaya Kumar K, Cho W, Prasad Joshi G, Won Kim S (2023) DNA computing with water strider based vector quantization for data storage systems. CMC-Comput Mater Cont 74(3):6429–6444

Kalabokidis K (2016) AEGIS: a wildfire prevention and management information system. Nat Hazards Earth Syst Sci 16:643–661CrossRef

Lin G et al (2019) Smoke detection on video sequences using 3D convolutional neural networks. Fire Technol 55:1827–1847CrossRef

Mangiameli M, Mussumeci G, Cappello A (2021) Forest fire spreading using free and open-source GIS technologies. Geomatics 1.1:50–64CrossRef

Muhammad K, Jamil A, Sung WB (2018) Early fire detection using convolutional neural networks during surveillance for effective disaster management. Neurocomputing 288:30–42CrossRef

Naderpour M et al (2019) Forest fire induced Natech risk assessment: a survey of geospatial technologies. Reliab Eng Syst Saf 191:106558CrossRef

Novkovic I et al (2021) GIS-based forest fire susceptibility zonation with IoT sensor network support, case study—Nature Park Golija. Serbia. Sensors 21.19:6520CrossRef

Pourghasemi H, Masood B, Biswajeet P (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomat Natl Haz Risk 7.2:861–885CrossRef

Purnomo EP, et al. (2021) Observed data of forest fire hotspots effects on respiratory disorder by Arc-GIS in Riau Province, Indonesia. In: IOP Conference Series: Earth and Environmental Science. vol. 717. No. 1. IOP Publishing

Rahimi I, Salim NA, Imran HA (2020) Mapping forest-fire potentiality using remote sensing and GIS, case study: Kurdistan Region-Iraq. Environ Remote Sens GIS Iraq: 499–513

Rao GN, Jagdeeswar Rao P (2014) A clustering analysis for heart failure alert system using RFID and GPS. In: ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India-Vol I. Springer, Cham

Rao GN, Ramesh R, Rajesh D, Chandra Sekhar D (2012) An automated advanced clustering algorithm for text classification. Int J Comput Sci Technol 3.2–4

Rao GN, Rao PJ, Duvvuru R (2016a) A drone remote sensing for virtual reality simulation system for forest fires: semantic neural network approach. In: IOP Conference Series: Materials Science and Engineering. vol. 149. no. 1. IOP Publishing

Rao GN et al (2016b) An enhanced real-time forest fire assessment algorithm based on video by using texture analysis. Perspect Sci 8:618–620CrossRef

Rao GN, et al (2018) Fire detection in Kambalakonda Reserved Forest, Visakhapatnam, Andhra Pradesh, India: an internet of things approach. J Mater Today Proc Elsevier 5(1): 1162–1168 (ISSN: 2214–7853)

Rao GN, et al (2019) Geo spatial study on fire risk assessment in kambalakonda reserved forest, Visakhapatnam, India: a clustering approach. In: Proceedings of International Conference on Remote Sensing for Disaster Management. Springer, Cham

Rao GN et al (2023) Geospatial study on forest fire disasters—a GIS approach. Ecol Eng 1:302–311

Saeed F et al (2020) Convolutional neural network based early fire detection. Multimed Tools Appl 79:9083–9099CrossRef

Seydi ST et al (2022) Fire-Net: a deep learning framework for active forest fire detection. J Sens 2022:1–14

Sivrikaya F, Küçük Ö (2022) Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Eco Inform 68:101537CrossRef

Soliman H, Sudan K, Noel MA, Ospino A, Lizardo Zelaya NA, Smart Forest Fire Early Varela A (2020) Wireless sensor network for forest fire detection. Proc Comput Sci 175:435–440CrossRef

Thapa S, et al. (2021) Forest fire detection and monitoring.In: Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region: A Decade of Experience from SERVIR, pp 147–167

Xu R et al (2021) A forest fire detection system based on ensemble learning. Forests 12.2:217CrossRef

Xue Z, Lin H, Wang F (2022) A small target forest fire detection model based on YOLOv5 improvement. Forests 13.8:1332CrossRef

Yuan C, Zhixiang L, Youmin Z (2017) Fire detection using infrared images for UAV-based forest fire surveillance. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE

Zhang Q, et al (2016) Deep convolutional neural networks for forest fire detection. In: 2016 International Forum on Management, Education and Information Technology Application. Atlantis Press

Zhao P et al (2021) GIS-based forest fire risk model: a case study in Laoshan National Forest Park, Nanjing. Remote Sens 13.18:3704CrossRef

Title: Neural fuzzy system design in forest fire detection
Authors: Gudikandhula Narasimha Rao
Peddada Jagadeeswara Rao
Rajesh Duvvuru
Kondapalli Beulah
E. Laxmi Lydia
Prasanthi Rathnala
Bangaru Balakrishna
Vijaya Raju Motru
Publication date: 28-06-2023
Publisher: Springer Berlin Heidelberg
Published in: Microsystem Technologies / Issue 4/2024
Print ISSN: 0946-7076
Electronic ISSN: 1432-1858
DOI: https://doi.org/10.1007/s00542-023-05496-9

Foreword: special issue on CCSN-22

Editorial

Preface- special issue CCSN-2022

Editorial

Parameter extraction of floating solar PV system with war strategy optimization for sustainable cleaner generation

Technical Paper

HCM: a hierarchical clustering framework with MOORA based cluster head selection approach for energy efficient wireless sensor networks

Technical Paper

Correction to: Quality aware cost efficient reward mechanism in mobile crowdsensing system with uncertainty constraints

Correction

Fundus vessel structure segmentation based on Bel-Hat transformation

Technical Paper