4.7 Article

Evaluation of Land Suitability for Olive (Olea europaea L.) Cultivation Using the Random Forest Algorithm

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Interdisciplinary Applications

Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey

Hazan Alkan Akinci et al.

Summary: This study aimed to produce forest fire susceptibility maps for the Manavgat district in Antalya province, Turkey using different machine learning techniques. Forest fire inventory data from 2013-2021 were obtained and 15 factors were used in the study. Tree-based ML models and artificial neural networks were used to produce the maps, and performance evaluation was done using various metrics. The XGBoost model was found to be the most suitable for fire prevention measures.

EARTH SCIENCE INFORMATICS (2023)

Article Geosciences, Multidisciplinary

Spatial prediction of flash flood susceptible areas using novel ensemble of bivariate statistics and machine learning techniques for ungauged region

Manish Singh Rana et al.

Summary: This study aimed to improve flash flood susceptibility modeling by incorporating ensemble approaches into bivariate and multivariate statistical models. A flash flood and geospatial database were developed, and weights were assigned to influencing factors based on correlation and weight of evidence (WOE) methods. Multiple models were built and validated, with the WOE-ANN model outperforming all machine learning models.

NATURAL HAZARDS (2023)

Article Geosciences, Multidisciplinary

Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping

Bilal Aslam et al.

Summary: This study assesses and researches the landslide susceptibility in northern Pakistan using various convolutional neural network (CNN) architectures and residual networks (ResNet). The results show that the CNN models are significantly helpful in preventing landslides, and have higher prediction accuracy than other traditional machine learning and deep learning techniques.

NATURAL HAZARDS (2023)

Article Environmental Sciences

Geographic information system-assisted site quality assessment for hazelnut cultivation using multi-criteria decision analysis in the Black Sea region, Turkey

Emre Tercan et al.

Summary: The goal of this study is to improve the land suitability model for hazelnut cultivation and apply it in the Unye District of Turkey. The results show that the coastal areas of the study area are the most suitable for hazelnut growing. The two main factors impacting the model are climatic and topographic conditions.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

Article Geosciences, Multidisciplinary

Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea

Yong Piao et al.

Summary: This study constructed a forest fire susceptibility map in Gangwon-do, Korea using Google Earth Engine and machine learning algorithms. The results identified slope, human activity, and interference as the important factors affecting forest fire occurrence in the region.

GEOMATICS NATURAL HAZARDS & RISK (2022)

Article Agronomy

Effects of Solar Radiation on Dry Matter Distribution and Root Morphology of High Yielding Maize Cultivars

Xiaoxia Guo et al.

Summary: This study investigated the effects of solar radiation on root morphology of different maize cultivars and found that large root systems were important for rapid response to decreased solar radiation and achieving stable and high yield.

AGRICULTURE-BASEL (2022)

Article Green & Sustainable Science & Technology

Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study

Aihua Wei et al.

Summary: This study compares popular ensemble machine learning-based models and applies them to landslide susceptibility mapping. The results show that several ensemble models can appropriately predict landslide susceptibility maps, with the XGBoost model performing the best.

SUSTAINABILITY (2022)

Article Ecology

Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey

Muzaffer Can Iban et al.

Summary: This study aims to generate Machine Learning (ML) based wildfire susceptibility maps for Adana and Mersin provinces in Turkey and evaluates the performance of different algorithms. The results indicate that the Random Forest model performs the best, and elevation, temperature, and slope are the most significant factors.

ECOLOGICAL INFORMATICS (2022)

Article Geosciences, Multidisciplinary

Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques

Halil Akinci

Summary: This study evaluated the performance of different machine learning models in rainfall-induced landslide susceptibility mapping, with the GBM model outperforming others.

JOURNAL OF AFRICAN EARTH SCIENCES (2022)

Article Communication

Prediction of Wheat Production Using Machine Learning Algorithms in northern areas of Pakistan

Moiz Uddin Ahmed et al.

Summary: This study presents a predictive model of wheat production using machine learning, which demonstrates the predictive ability of machine learning algorithms on a crop dataset recorded in a localized environment.

TELECOMMUNICATIONS POLICY (2022)

Article Environmental Sciences

Climate Change over the Mediterranean Region: Local Temperature and Precipitation Variations at Five Pilot Sites

Valeria Todaro et al.

Summary: This study analyzes historical and future climate changes in the Mediterranean region using 17 regional climate models. The results indicate a progressive and robust warming trend, with moderate changes in precipitation and seasonal variations. The study also predicts an increase in drought events, including longer heat waves and consecutive dry days.
Article Agronomy

Qualitative Evaluation of Land Suitability for Olive, Potato and Cotton Cultivation in Tarom in Zanjan

Zahra Mirzae Shiri et al.

Summary: This study aims to evaluate the suitability of land for cotton, potato, and olive crops in Tadarom, Tarom region, Zanjan province. After sampling and testing, the soils were classified and climate, soil, and terrain data were collected. Land suitability assessment for the crops was performed using different methods, and the results showed that the area was relatively suitable for olive cultivation but needed improvement in drainage and the amount of gravel.

AGRITECH (2022)

Article Agriculture, Multidisciplinary

Expansion of the olive crop based on modeling climatic variables using geographic information system (GIS) in Aljouf region KSA

Hamoud H. Alshammari et al.

Summary: This study uses a scientific approach to expand olive cultivation by modeling environmental factors, successfully identifying new lands suitable for olive growth with a high success rate. It also reinforces the concept of Land-use/land-cover (LULC), focusing on the change of land from one state to another over time series according to climatic changes.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

Article Environmental Sciences

Detection and Counting of Corn Plants in the Presence of Weeds with Convolutional Neural Networks

Canek Mota-Delfin et al.

Summary: This study developed a method using aerial RGB images and deep learning algorithms to detect and count corn plants, and compared the performance of different detectors.

REMOTE SENSING (2022)

Article Mathematical & Computational Biology

An Efficient Deep Learning Mechanism for the Recognition of Olive Trees in Jouf Region

Hamoud H. Alshammari et al.

Summary: Olive trees grow worldwide with cultural and economic significance. Using deep learning and remote sensing data can accurately estimate the biovolume of olive trees, aiding in monitoring their productivity and health.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2022)

Article Agronomy

Suitability Evaluation of Tea Cultivation Using Machine Learning Technique at Town and Village Scales

Wenwen Xing et al.

Summary: Suitability evaluation of tea cultivation is crucial for improving yield and quality while ensuring sustainable development. This study developed a machine learning-based model to assess tea cultivation suitability at a town and village scale. By comparing different algorithms, 12 factors were selected and evaluated using the random forest algorithm. The results provide a scientific reference for land allocation decisions and sustainable agricultural development.

AGRONOMY-BASEL (2022)

Article Engineering, Chemical

Land suitability mapping for rainfed olive tree plantation in the West Bank, Palestine

Sameer Shadeed et al.

Summary: This study assessed the suitability of land for olive tree plantation in the West Bank, Palestine, using GIS-based multi-criteria decision analysis. The results showed that approximately 47% of the area is highly suitable for olive cultivation, with Tulkarm, Qalqiliya, and Salfit areas being 100% suitable. The study also found that the highly suitable areas are mainly located in areas under Palestinian administrative control, providing feasible opportunities for olive tree expansion.

DESALINATION AND WATER TREATMENT (2022)

Article Environmental Studies

Land Suitability Analysis for Vineyard Cultivation in the Izmir Metropolitan Area

Stefano Salata et al.

Summary: The study establishes a land suitability analysis for vineyard production in the metropolitan area of Izmir, Turkey, using elevation, slope, aspect, land capability, and solar radiation as criteria. The findings provide insights into the most suitable areas for vineyard production and future strategic expansion and management. The discussion focuses on promoting a new policy of vineyard plantation in the Kozak plateau to preserve traditional methods and practices.
Article Multidisciplinary Sciences

Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)

Taskin Kavzoglu et al.

Summary: In this study, a novel ensemble learning method called NGBoost was proposed for landslide susceptibility modeling in Turkey. The predictive performance of NGBoost was compared to other ensemble learning methods, and the results showed that NGBoost had the highest predictive ability and outperformed other methods by approximately 6% in terms of overall accuracy.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2022)

Article Geosciences, Multidisciplinary

Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt

Bosy A. El-Haddad et al.

Summary: The study utilized four data mining/machine learning models to generate flood susceptibility maps, assessed the relationship between flood-influencing factors and flood distribution through different datasets and models, and found that the models provide reasonable accuracy in flood susceptibility mapping.

NATURAL HAZARDS (2021)

Review Agronomy

An Overview of Olive Cultivation in Turkey: Botanical Features, Eco-Physiology and Phytochemical Aspects

Munir Ozturk et al.

Summary: Global climate change, especially global warming, is affecting the efficiency and quality of olive production. Olive trees prefer mild winters, short rainy seasons, and well-drained nutrient-rich soils, but are sensitive to long and dry summers, harsh winters, and air pollutants. Adequate temperature and soil conditions are crucial for optimal growth and fruit yield of olive trees.

AGRONOMY-BASEL (2021)

Article Geochemistry & Geophysics

Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model

Halil Akinci et al.

Summary: This study evaluated landslide susceptibility of the Central district of Artvin in Turkey using the frequency ratio (FR) and modified information value (MIV) models. Results showed that the MIV model performed slightly better than the FR model in terms of prediction and success rates. Additionally, it was found that most village built-up areas and majority of the planned areas within the municipal boundaries in Artvin were located in landslide susceptible zones.

ACTA GEOPHYSICA (2021)

Article Geosciences, Multidisciplinary

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

Halil Akinci et al.

Summary: This study compared the performance of machine learning models and traditional statistical methods in producing landslide susceptibility maps, with machine learning models showing higher success and prediction rates, especially with the RF model performing the best.

NATURAL HAZARDS (2021)

Article Geosciences, Multidisciplinary

Determination of Hazelnut Gardens by Pixel Based Classification Methods Using Sentinel-2 Data

Ceyhun Apaydin et al.

Summary: This study aimed to determine hazelnut orchards in Besikduzu District of Trabzon province in Turkey using Sentinel-2 satellite images. Three different machine learning algorithms were applied with high classification accuracy.

GEOMATIK (2021)

Article Environmental Sciences

Projected climate changes are expected to decrease the suitability and production of olive varieties in southern Spain

Salvador Arenas-Castro et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2020)

Article Computer Science, Interdisciplinary Applications

Comparative study of landslide susceptibility mapping with different recurrent neural networks

Yi Wang et al.

COMPUTERS & GEOSCIENCES (2020)

Article Environmental Sciences

Determination of olive cultivars by deep learning and ISSR markers

M. Sesli et al.

JOURNAL OF ENVIRONMENTAL BIOLOGY (2020)

Review Geosciences, Multidisciplinary

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

Abdelaziz Merghadi et al.

EARTH-SCIENCE REVIEWS (2020)

Article Computer Science, Information Systems

Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

Halil Akinci et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2020)

Article Agriculture, Multidisciplinary

GIS based land suitability assessment for tobacco production using AHP and fuzzy set in Shandong province of China

Jiuquan Zhang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2015)

Article Agriculture, Multidisciplinary

Agricultural land use suitability analysis using GIS and AHP technique

Halil Akinci et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2013)

Proceedings Paper Engineering, Environmental

A Comparison of Parametric and Fuzzy Multi-Criteria Methods for Evaluating Land Suitability for Olive in Jeffara Plain of Libya

Mukhtar Elaalem

4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL SCIENCE AND DEVELOPMENT- ICESD 2013 (2013)

Article Computer Science, Interdisciplinary Applications

Application of an evidential belief function model in landslide susceptibility mapping

Omar F. Althuwaynee et al.

COMPUTERS & GEOSCIENCES (2012)

Article Computer Science, Interdisciplinary Applications

Using GIS and Fuzzy Sets to Evaluate the Olive Tree's Ecological Suitability in Sichuan Province

Xiang Guo et al.

COMPUTING IN SCIENCE & ENGINEERING (2010)

Article Agriculture, Multidisciplinary

Using fuzzy data mining to evaluate survey data from olive grove cultivation

G. Delgado et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2009)

Article Computer Science, Interdisciplinary Applications

Building Predictive Models in R Using the caret Package

Max Kuhn

JOURNAL OF STATISTICAL SOFTWARE (2008)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)