Comparative study on  machine learning algorithms  for early fire forest detection system using geodata.

Zouiten, Mohammed; Hanae, Chaaouan; Setti, Larbi

doi:10.11591/ijece.v10i5.pp5507-5513

Cited by 14 publications

(6 citation statements)

References 48 publications

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“…A uniform image with one grey level value will have a maximum energy value of 1. The fourth feature, we calculate the smoothness of intensity in the image as shown in (5). In (5), if the S value is low, the image has a rough intensity.…”

Section: 𝜇 = ∑ 𝑗 𝑝𝑟(𝑗)mentioning

confidence: 99%

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Classification of six banana ripeness levels based on statistical features on machine learning approach

Kosasih,

Sudaryanto,

Fahrurozi

2023

IJAAS

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Banana plants are often cultivated because they have many benefits. In producing, we need to maintain the quality of bananas by looking at banana ripeness levels before being distributed to markets. The level of banana ripeness is related to marketing reach. If the marketing reach is far, bananas should be harvested when the ripeness level of bananas is still relatively low. A system that can classify the degree of ripeness of bananas can help overcome this problem. In this study, our dataset includes 6 ripeness levels of bananas, more than in previous related studies. Furthermore, we use the statistical features extraction method to find the parameters that affect the level of banana ripeness, considering the texture and color of the banana peel which determines the level of ripeness visually. The extraction used is features extraction based on a histogram, then we employ four features, i.e., mean, skewness, energy descriptor, and smoothness, generated from the image dataset. In the next stage, we perform classification based on the features that have been obtained. In this study, we use Naive Bayes classifier and support vector machine (SVM) algorithms. Based on the result of this research, the best performance is the Naive Bayes classifier, with an accuracy is 86.67%, a weighted average precision of 83.55%, and a weighted average recall of 86.67%.

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Section: 𝜇 = ∑ 𝑗 𝑝𝑟(𝑗)mentioning

confidence: 99%

“…A machine learning algorithm is one part of artificial intelligence used to perform learning based on training data. Based on this learning, machine learning can perform classification [4], [5]. Several machine learning models can be used to classify, i.e., Naive Bayes classifier [6], [7] and support vector machine (SVM) [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Classification of six banana ripeness levels based on statistical features on machine learning approach

Kosasih,

Sudaryanto,

Fahrurozi

2023

IJAAS

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“…Mohammed et al [19] conducted a comparative study of machine learning algorithms for early forest fire detection using geodata. It proposed a spatial prediction model for real-time identification of fire risk zones, alerting authorities and presenting geographical treatments for enhanced efficacy.…”

Section: Introductionmentioning

confidence: 99%

Real-time forest fire detection, monitoring, and alert system using Arduino

Ikhwan Mohd Anuar,

Mohamad,

Munira Markom

et al. 2024

IJEECS

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<div align="center">Early fire detection is critical to protecting forests from wildfires and enabling rapid responses to minimize fire spread. Existing forest fire detection methods cannot quickly detect forest fires and evaluate the fire risk of these sensitive areas. Hence, this research aims to develop a real-time forest fire detection, monitoring, and alert system. The development of the system started with assembling temperature and humidity sensors, a smoke sensor, an Arduino microcontroller, and a wireless fidelity module. Then, a fire monitoring and alert system was developed using Blynk. From the sensitivity flame sensor analysis with the fire, the flame sensor detected the presence of fire up to 60 cm. The sensor also indicated high temperature (45 °C) and low humidity (53.4%) at noon. Low temperature (29 ℃) and high humidity (88.4%) were identified in the morning. Moreover, the highest carbon dioxide (CO2) concentration of 1,800 ppm was recorded when the smoke from the fire was detected. The global positioning system module shows the accurate real-time location of the system displayed in the Blynk application. In conclusion, this system can detect and monitor early forest fires in real-time and can alert the authorities to protect forests from wildfires.</div>

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“…Statistical methods such as bivariate and multivariate analysis [32,33], multiple linear regression [34,35], and logistic regression [36][37][38][39] have been widely used for forest fre modelling. In recent years, machine learning algorithms in forest fre risk analysis have also gained popularity [40][41][42][43][44]. Rodrigues and De La Riva [45] developed random forest (RF), boosting regression trees (BRT), and support vector machine (SVM) algorithms in their study in the region covering almost the entire Spanish peninsula.…”

Section: Introductionmentioning

confidence: 99%

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

Abujayyab

Kassem

Khan

et al. 2022

Advances in Civil Engineering

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Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.

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Comparative study on machine learning algorithms for early fire forest detection system using geodata.

Cited by 14 publications

References 48 publications

Classification of six banana ripeness levels based on statistical features on machine learning approach

Classification of six banana ripeness levels based on statistical features on machine learning approach

Real-time forest fire detection, monitoring, and alert system using Arduino

Wildfire Susceptibility Mapping Using Five Boosting Machine Learning Algorithms: The Case Study of the Mediterranean Region of Turkey

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