Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods

Razavi-Termeh, Seyed Vahid; Sadeghi‐Niaraki, Abolghasem; Choi, Soo-Mi

doi:10.3390/rs12101689

Cited by 59 publications

(29 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To determine the effect of each class, each variable independent of Eq. ( 5 ) is used 34 . where FR is the effect of each class of each parameter, the percentage of training points located in class i, and the percentage of the pixels of class i in the entire study area.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Asthma-prone areas modeling using a machine learning model

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran considering environmental, spatial factors. Initially, we built a spatial database using 872 locations of children with asthma and 13 environmental factors affecting the disease—distance to parks and streets, rainfall, temperature, humidity, pressure, wind speed, particulate matter (PM 10 and PM 2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), and nitrogen dioxide (NO2). Subsequently, utilizing this spatial database, a random forest (RF) machine learning model, and a geographic information system, we prepared a map of asthma-prone areas. For modeling and validation, we deployed 70% and 30%, respectively, of the locations of children with asthma. The results of spatial autocorrelation and RF model showed that the criteria of distance to parks and streets as well as PM 2.5 and PM 10 had the greatest impact on asthma occurrence in the study area. Spatial autocorrelation analyses indicated that the distribution of asthma cases was not random. According to receiver operating characteristic results, the RF model had good accuracy (the area under the curve was 0.987 and 0.921, respectively, for training and testing data).

show abstract

Section: Methodsmentioning

confidence: 99%

“…The area below the ROC curve is called AUC. Its value varies between 0.5 and 1; the closer it is to one, the higher the modeling efficiency is 34 .…”

Section: Methodsmentioning

confidence: 99%

Asthma-prone areas modeling using a machine learning model

Razavi-Termeh

Sadeghi‐Niaraki

Choi

2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Besides, the results show that the use of intelligent sensors and IoT for real-time detection in applications like fire action (R15.4) has received lower attention. Contrary to other SDGs' recommendations related to IoT with very favourable results, this outcome is surprising since such a technology, even when still needs development to reach greater level of maturity, has demonstrated a strong efficiency and impact on fire detection [29][30][31] and land health monitoring [32][33][34]. Notwithstanding, it is important to remark that this result should be interpreted in terms of relative, pairwise preferences among the five candidate recommendations.…”

Section: Sdg 13: Climate Actionmentioning

confidence: 66%

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

et al. 2021

View full text Add to dashboard Cite

The United Nations Agenda 2030 established 17 Sustainable Development Goals (SDGs) as a guideline to guarantee a sustainable worldwide development. Recent advances in artificial intelligence and other digital technologies have already changed several areas of modern society, and they could be very useful to reach these sustainable goals. In this paper we propose a novel decision making model based on surveys that ranks recommendations on the use of different artificial intelligence and related technologies to achieve the SDGs. According to the surveys, our decision making method is able to determine which of these technologies are worth investing in to lead new research to successfully tackle with sustainability challenges.

show abstract

“…The results from these studies show that the FLM has better prediction accuracy. The MARS-DFP, fuzzy AHP, and ANFIS models have also been applied to forest fire susceptibility (FFS) studies (Jang et al 1998;Razavi-Termeh et al, 2020). The ensemble model and MARS-DFP models were the best models in this study.…”

Section: Comparison Of Modelsmentioning

confidence: 81%

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Saha¹,

Gayen²,

Mukherjee³

et al. 2021

Preprint

View full text Add to dashboard Cite

Machine learning techniques offer powerful tools for the assessment and management of groundwater resources. Here, we evaluated the groundwater potential maps (GWPMs) in Md. Bazar Block of Birbhum District, India using four GIS-based machine-learning algorithms (MLA) such as predictive neural network (PNN), decision tree (DT), Naïve Bayes classifier (NBC), and random forest (RF). We used a database of 85 dug wells and one piezometer location identified using extensive field study, and employed 12 influencing factors (elevation, slope, drainage density (DD), topographical wetness index, geomorphology, lineament density, rainfall, geology, pond density, land use/land cover (LULC), geology, and soil texture) for evaluation through GIS. The 85 dug wells and 1 piezometer locations were sub-divided into two classes: 70:30 for training and model validation. The DT, RF, PNN, and NBC MLAs were implemented to analyse the relationship between the dug well locations and groundwater influencing factors to generate GWPMs. The results predict excellent groundwater potential areas (GPA) DT RF of 17.38%, 14.69%, 20.43%, and 13.97% of the study area, respectively. The prediction accuracy of each GWPM was determined using a receiver operating characteristic (ROC) curve. Using the 30% data sets (validation data), accuracies of 80.1%, 78.30%, 75.20%, and 69.2% were obtained for the PNN, RF, DT, and NBC models, respectively. The ROC values show that the four implemented models provide satisfactory and suitable results for GWP mapping in this region. In addition, the well-known mean decrease Gini (MDG) from the RF MLA was implemented to determine the relative importance of the variables for groundwater potentiality assessment. The MDG revealed that drainage density, lineament density, geomorphology, pond density, elevation, and stream junction frequency were the most useful determinants of GWPM. Our approach to delineate the GWPM can aid in the effective planning and management of groundwater resources in this region.

show abstract

Ubiquitous GIS-Based Forest Fire Susceptibility Mapping Using Artificial Intelligence Methods

Cited by 59 publications

References 67 publications

Asthma-prone areas modeling using a machine learning model

Asthma-prone areas modeling using a machine learning model

Ordering Artificial Intelligence Based Recommendations to Tackle the SDGs with a Decision-Making Model Based on Surveys

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Contact Info

Product

Resources

About