Machine Learning in Agriculture: A Comprehensive Updated Review

Sensors, Journal Year: 2021, Volume and Issue: 21(11), P. 3758 - 3758

Published: May 28, 2021

The digital transformation of agriculture has evolved various aspects management into artificial intelligent systems for the sake making value from ever-increasing data originated numerous sources. A subset intelligence, namely machine learning, a considerable potential to handle challenges in establishment knowledge-based farming systems. present study aims at shedding light on learning by thoroughly reviewing recent scholarly literature based keywords’ combinations “machine learning” along with “crop management”, “water “soil and “livestock accordance PRISMA guidelines. Only journal papers were considered eligible that published within 2018–2020. results indicated this topic pertains different disciplines favour convergence research international level. Furthermore, crop was observed be centre attention. plethora algorithms used, those belonging Artificial Neural Networks being more efficient. In addition, maize wheat as well cattle sheep most investigated crops animals, respectively. Finally, variety sensors, attached satellites unmanned ground aerial vehicles, have been utilized means getting reliable input analyses. It is anticipated will constitute beneficial guide all stakeholders towards enhancing awareness advantages using contributing systematic topic.

Language: Английский

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Flood susceptibility modelling using advanced ensemble machine learning models

Geoscience Frontiers, Journal Year: 2020, Volume and Issue: 12(3), P. 101075 - 101075

Published: Oct. 5, 2020

Floods are one of nature's most destructive disasters because the immense damage to land, buildings, and human fatalities. It is difficult forecast areas that vulnerable flash flooding due dynamic complex nature floods. Therefore, earlier identification flood susceptible sites can be performed using advanced machine learning models for managing disasters. In this study, we applied assessed two new hybrid ensemble models, namely Dagging Random Subspace (RS) coupled with Artificial Neural Network (ANN), Forest (RF), Support Vector Machine (SVM) which other three state-of-the-art modelling susceptibility maps at Teesta River basin, northern region Bangladesh. The application these includes twelve influencing factors 413 current former points, were transferred in a GIS environment. information gain ratio, multicollinearity diagnostics tests employed determine association between occurrences influential factors. For validation comparison ability predict statistical appraisal measures such as Freidman, Wilcoxon signed-rank, t-paired Receiver Operating Characteristic Curve (ROC) employed. value Area Under (AUC) ROC was above 0.80 all models. modelling, model performs superior, followed by RF, ANN, SVM, RS, then several benchmark approach solution-oriented outcomes outlined paper will assist state local authorities well policy makers reducing flood-related threats also implementation effective mitigation strategies mitigate future damage.

Language: Английский

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Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

Stochastic Environmental Research and Risk Assessment, Journal Year: 2020, Volume and Issue: 34(12), P. 2277 - 2300

Published: Sept. 4, 2020

Language: Английский

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Evaluation of different boosting ensemble machine learning models and novel deep learning and boosting framework for head-cut gully erosion susceptibility

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 284, P. 112015 - 112015

Published: Jan. 27, 2021

Language: Английский

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A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Geomatics Natural Hazards and Risk, Journal Year: 2021, Volume and Issue: 12(1), P. 1741 - 1777

Published: Jan. 1, 2021

Landslides are dangerous events which threaten both human life and property. The study aims to analyze the landslide susceptibility (LS) in Kysuca river basin, Slovakia. For this reason, previous were analyzed with 16 conditioning factors. Landslide inventory was divided into training (70% of locations) validating dataset (30% locations). heuristic approach Fuzzy Decision Making Trial Evaluation Laboratory (FDEMATEL)-Analytic Network Process (ANP) applied first, followed by bivariate Frequency Ratio (FR), multivariate Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes (NBC) Extreme Gradient Boosting (XGBoost), respectively. results showed that 52.2%, 36.5%, 40.7%, 50.6%, 43.6% 40.3% total basin area had very high LS corresponding FDEMATEL-ANP, FR, LR, RFC, NBC XGBoost model, analysis revealed RFC most accurate model (overall accuracy 98.3% AUC 97.0%). Besides, FDEMATEL-ANP 93.8% 92.4%) better prediction capability than FR 86.9% 86.1%), LR 90.5% 91.2%), machine learning 76.3% 90.9%) even deep 92.3% 87.1%) models. outweighed models, suggests methods should be tested out before directly applying

Language: Английский

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Decision tree based ensemble machine learning approaches for landslide susceptibility mapping

Geocarto International, Journal Year: 2021, Volume and Issue: 37(16), P. 4594 - 4627

Published: March 5, 2021

The concept of leveraging the predictive capacity predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores different approaches to LS modelling using artificial intelligence. key objective this study is estimate a map Taleghan-Alamut basin Iran Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost CDT-SubSpace) hybrid approaches, which are state-of-the-art soft computing that hardly ever utilized assessment LS. In study, we used eighteen (LPFs) considered be most important local morphological geo-environmental influencing occurrence landslides. We calculated significance each LPFs Random Forest Method. also employed Receiver Operating Characteristic curve, precision, performance, robustness measurement selection best-fitting models. results shows that, compared other models, excellent model perspective with an average area under curve (AUC) 0.993 based 4-fold cross-validation. We, therefore, consider models effective method improving spatial prediction where scarps or bodies not clearly identified during preparation inventory maps. Therefore, it will helpful preparing future maps mitigate damages.

Language: Английский

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Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms

Advances in Space Research, Journal Year: 2023, Volume and Issue: 72(2), P. 426 - 443

Published: March 21, 2023

Language: Английский

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Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential

Geocarto International, Journal Year: 2021, Volume and Issue: 37(19), P. 5564 - 5584

Published: April 23, 2021

Delineation of the groundwater’s potential zones is a growing phenomenon worldwide due to high demand for fresh groundwater. Therefore, identification groundwater an important tool occurrence, protection, and management purposes. More specifically, in arid semi-arid regions, one most natural resources as it supplies water during drought period. The present research study focused on delineation Saveh City, northern part Markazi Province Iran. mapping was prepared using hybrid deep learning machine algorithm boosted tree (BT), artificial neural network (ANN), (DLNN), (DLT), boosting (DB). This carried out by fourteen conditioning factors 349 each springs non-springs points. performance model validated through statistical analysis sensitivity, specificity, positive predictive values (PPV), negative (NPV), receiver operating characteristic (ROC)-area under curve (AUC) analysis. validation result showed that success rate AUC very good DB (0.87–0.99) other models are also i.e. BT (0.81–0.90), ANN (0.77–0.82), DLNN (0.84–0.86), DLT (0.83–0.91). Among several used this altitude, rainfall, distance fault soil types more modeling. Finally, all had efficiency mapping, but recommended use Deep Boost better results future studies. work will be useful planners optimal planning

Language: Английский

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Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(23), P. 3854 - 3854

Published: Nov. 25, 2020

The main objective of the present study is to introduce a novel predictive model that combines evolutionary algorithms and machine learning (ML) models, so as construct landslide susceptibility map. Genetic (GA) are used feature selection method, whereas particle swarm optimization (PSO) method optimize structural parameters two ML support vector machines (SVM) artificial neural network (ANN). A well-defined spatial database, which included 335 landslides twelve landslide-related variables (elevation, slope angle, aspect, curvature, plan profile topographic wetness index, stream power distance faults, river, lithology, hydrological cover) considered for analysis, in Achaia Regional Unit located Northern Peloponnese, Greece. outcome illustrates both models have an excellent performance, with SVM achieving highest accuracy (0.977 area under receiver operating characteristic curve value (AUC)), followed by ANN (0.969). However, shows prediction (0.800 AUC), (0.750 AUC) model. Overall, proposed highlights necessity tuning procedures via such approaches could be successfully mapping alternative investigation tool.

Language: Английский

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Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

Earth Science Informatics, Journal Year: 2020, Volume and Issue: 14(1), P. 51 - 67

Published: Oct. 15, 2020

Language: Английский

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