Decision Tree Regression Models Research Articles

Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques

INTRODUCTION: Random Forests are an important model in machine learning. They are simple and very effective classification approach. The random forest identifies the most important features of a given problem.OBJECTIVES: The heart disease is cardiovascular disease, with a set of conditions affecting the heart. During heart disease, there will be heartbeat problems with congenital heart disorders and coronary artery defects. A coronary heart defect is a heart disease, which decreases the flow of blood to the heart. When the flow of blood decreases heart attack occurs. It is necessary to analyse the prediction of heart attack based on the symptoms.METHODS: The available data set of patients with heart defects symptoms is taken and analysed in this paper using the random forest and decision tree regression models. The missing data is updated using mean value of the attribute. Python language is used to predict the accuracy.RESULTS: Three performance measures are taken for analysing the available UCI Cleveland data set for heart disease. The performance measures are the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Vital attributes of the data set are taken for analyses using the random forest regression model and decision tree regression model. The analyses shows that the slope attribute provides the better prediction for the heart disease. The results are shows that the females are more prone to heart attack.CONCLUSION: Prediction of heart disease using the UCI machine learning data set at Cleveland repository is analysed using random forest regression model and decision tree regression models. We find random forest regression model provides better accuracy than decision tree regression model.

CPRQ: Cost Prediction for Range Queries in Moving Object Databases

Predicting query cost plays an important role in moving object databases. Accurate predictions help database administrators effectively schedule workloads and achieve optimal resource allocation strategies. There are some works focusing on query cost prediction, but most of them employ analytical methods to obtain an index-based cost prediction model. The accuracy can be seriously challenged as the workload of the database management system becomes more and more complex. Differing from the previous work, this paper proposes a method called CPRQ (Cost Prediction of Range Query) which is based on machine-learning techniques. The proposed method contains four learning models: the polynomial regression model, the decision tree regression model, the random forest regression model, and the KNN (k-Nearest Neighbor) regression model. Using R-squared and MSE (Mean Squared Error) as measurements, we perform an extensive experimental evaluation. The results demonstrate that CPRQ achieves high accuracy and the random forest regression model obtains the best predictive performance (R-squared is 0.9695 and MSE is 0.154).

Iowa Liquor Sales Data Predictive Analysis Using Spark

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

Battery State of Health and Charge Estimation Using Machine Learning Methods

In this study, state of health (SOH) and state of charge (SOC) estimation of series connected batteries were evaluated for their charge and discharge durations. For this purpose, an ARM-based electronics card module was developed for observing instantaneous batteries voltage, current and temperature values during the charge and discharge process. The implemented microcontroller based card module gathers data from the current, voltage, and temperature sensors and it transfers to the computer environment via serial communication port. A specific human machine interface is designed via app-designer. The obtained variables were used for estimating regression models of the machine-learning toolbox. Random forest, decision tree, polynomial, extreme gradient boosting, linear and gradient boosting regression models were used for instantaneous SOH and SOC estimation for batteries during the charge-discharge period. Root Mean Square Error (RMSE) and R^2score results were used for performance evaluation of regression models. When the RMSE and R^2 score results were compared, the decision tree regression model was the regression model that made the most accurate SOH and SOC estimation and the results were presented.

Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism

In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism ( f p ). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength ( f m ), concrete block compressive strength ( f b ), and height to thickness ratio ( h/t ). Three modeling scenarios based on the different data divisions (i.e., 80–20%, 75–25%, and 70–30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80–20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively.

Solar radiation prediction using boosted decision tree regression model: A case study in Malaysia.

Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2-4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources.

Cutting power and surface quality in sawing kiln-dried, green, and frozen hem-fir wood

The lumber manufacturing industry faces challenges due to the cutting of wood with a high variation in moisture content and temperature. In this study, the cutting power and waviness during the circular sawing process of kiln-dried, green, and frozen hem-fir wood were measured and compared under different feed and rotation speeds. An analysis of variance (ANOVA) was performed to evaluate the impact of cutting parameters and the wood condition (kiln-dried, green and frozen), and a decision tree regression model was developed to predict the cutting power and waviness. The ANOVA highlighted that the feed speed had the most impact on cutting power, followed by the wood condition. Similar results were obtained from the decision tree regression (R2 = 0.89). The developed model failed to accurately predict the waviness from the cutting parameters, which emphasized the need for online wood sawing monitoring systems and a deeper understanding of saw blade’s dynamic behavior. The role of moisture content in the cutting power and waviness was nonlinear. The highest cutting power corresponded to sawing frozen wood, while there was no significant difference between sawing dry and green wood. The largest values of waviness were associated with sawing of dry wood, whereas sawing frozen and green wood yielded similar waviness. Sawing at higher rotation speeds yielded an increase in the cutting power but improved the surface quality by lowering the waviness.

Blood Uric Acid Prediction With Machine Learning: Model Development and Performance Comparison.

BackgroundUric acid is associated with noncommunicable diseases such as cardiovascular diseases, chronic kidney disease, coronary artery disease, stroke, diabetes, metabolic syndrome, vascular dementia, and hypertension. Therefore, uric acid is considered to be a risk factor for the development of noncommunicable diseases. Most studies on uric acid have been performed in developed countries, and the application of machine-learning approaches in uric acid prediction in developing countries is rare. Different machine-learning algorithms will work differently on different types of data in various diseases; therefore, a different investigation is needed for different types of data to identify the most accurate algorithms. Specifically, no study has yet focused on the urban corporate population in Bangladesh, despite the high risk of developing noncommunicable diseases for this population.ObjectiveThe aim of this study was to develop a model for predicting blood uric acid values based on basic health checkup test results, dietary information, and sociodemographic characteristics using machine-learning algorithms. The prediction of health checkup test measurements can be very helpful to reduce health management costs.MethodsVarious machine-learning approaches were used in this study because clinical input data are not completely independent and exhibit complex interactions. Conventional statistical models have limitations to consider these complex interactions, whereas machine learning can consider all possible interactions among input data. We used boosted decision tree regression, decision forest regression, Bayesian linear regression, and linear regression to predict personalized blood uric acid based on basic health checkup test results, dietary information, and sociodemographic characteristics. We evaluated the performance of these five widely used machine-learning models using data collected from 271 employees in the Grameen Bank complex of Dhaka, Bangladesh.ResultsThe mean uric acid level was 6.63 mg/dL, indicating a borderline result for the majority of the sample (normal range <7.0 mg/dL). Therefore, these individuals should be monitoring their uric acid regularly. The boosted decision tree regression model showed the best performance among the models tested based on the root mean squared error of 0.03, which is also better than that of any previously reported model.ConclusionsA uric acid prediction model was developed based on personal characteristics, dietary information, and some basic health checkup measurements. This model will be useful for improving awareness among high-risk individuals and populations, which can help to save medical costs. A future study could include additional features (eg, work stress, daily physical activity, alcohol intake, eating red meat) in improving prediction.

Lake water level modeling using newly developed hybrid data intelligence model

The forecasting of lake water level is one of the complex problems in the hydrology field owing to the incorporating with various hydrological and morphological characteristics. In this research, newly hybrid data intelligence (DI) model based on the integration of the Multilayer Perceptron (MLP) and Whale Optimization Algorithm (WOA) is developed for lake water level forecasting. The potential of the proposed hybrid MLP_WOA model is validated against several well-established DI models over the literature including the Cascade-Correlation Neural Network Model (CCNNM), Self-Organizing Map (SOM), Decision Tree Regression (DTR), Random Forest Regression (RFR), and classical MLP. The applied predictive models are examined to forecast the Van Lake water level fluctuation with monthly scale over seven-decade time period (1943–2016). The input variables are abstracted using statistical correlation analysis procedure. The modeling is diagnosed using multiple statistical metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE), Willmott’s Index (WI), Legate and McCabe’s Index (LMI), determination coefficient (R2)). In addition, graphical distribution data such as the Taylor diagram, violin plot, and point density are investigated. Results indicated that the MLP_WOA model performed superior prediction results over the comparable models based on forecasting performance. Five-month lead times performed the best results for the prediction procedure. In quantitative terms, the RMSE and MAE are reduced by 29.8% and 33.9%, 48.3% and 52%, 57.6% and 59.7%, 53.9% and 58.3%, and 25.3% and 23.9% using the MLP_WOA model over CCNNM, SOM, DTR, RFR, and MLP models, respectively. In comparison with the literature studies, using longer span of historical data elevated the forecasting accuracy. In summary, MLP_WOA model provided an applicable and simple methodology for Van Lake water level forecasting owing to its simple learning procedure.

Real-time prediction of projectile penetration to laminates by training machine learning models with finite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simulation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed; the residual velocities of projectiles from finite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340+polyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with the finite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, because finite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.

Decision tree regression model to predict low-rank coal moisture content during convective drying process

ABSTRACT Coal is still a significant energy source for the world. Due to the utilization of low-rank coal, drying is a key issue. There are lots of attempts to develop efficient drying processes. The most prominent method seems as thermal drying. For thermal drying processes, the most important subject is the coal moisture content change with time. In this study, convective drying experiments were utilized to develop a new model based on decision tree regression method to predict coal moisture content. The developed model gives satisfactory results in prediction of instant coal moisture content with changing drying conditions. With the decision tree depth of six, the best test results were achieved as 0.056 and 0.802 for MSE and R 2 analyses, respectively.

A hybrid multi‐output‐predictive modelling based NSGA II approach for dimensions design optimization of battery pack module for electric vehicles

AbstractBattery module safety is a major concern for the commercial success of electric vehicles (EVs). Concurrently, it is also important to have a mechanically sound and ergonomically fit battery pack design. To solve this problem, a hybrid multi‐output‐predictive modelling based NSGA II approach is proposed. In this approach, the multiple predictive modelling methods (linear regression, regression with AdaBoost, decision tree regression and multi‐layer perceptron [MLP]) are applied to predict the deformation, natural frequency and mass of battery pack enclosure. By performing the comparative analysis of these methods, the decision tree regression model was selected for deformation, MLP with tanh function for frequency and MLP with ReLU function for mass. Further, these selected models were optimized using NSGA II which resulted in optimum combination of input variables for achieving the maximum deformation (0.0019 m), minimum natural frequency (91.60 Hz), and mass (12.41 kg) simultaneously.

Prediction of Nonsinusoidal AC Loss of Superconducting Tapes Using Artificial Intelligence-Based Models

Current is no longer sinusoidal in modern electric networks because of widespread use of power electronic-based equipments and nonlinear loads. Usually AC loss is calculated for pure sinusoidal current, while it is no longer accurate when current is nonsinusoidal. On the other hand, efficiency of cooling system in large scale power devices is dependent on accurate estimation and prediction of the heat load caused by AC loss in design stage. Therefore, estimation of nonsinusoidal AC loss of high temperature superconducting (HTS) material would be of great interest for designers of large-scale superconducting devices. In this paper, at first nonsinusoidal AC loss of a typical HTS tape was calculated under distorted currents using H-formulation finite element method. Then, a range of artificial intelligence (AI) models were implemented to predict AC loss of a typical HTS tape. In order to find the best and more adaptive AI model for nonsinusoidal AC loss prediction, different regression models are evaluated using Support Vector Machine regression model, Generalized Linear regression model, Decision Tree regression model, Feed Forward Neural Network based model, Adaptive Neuro Fuzzy Inference System based model, and Radial Basis Function Neural Network (RBFNN) based model. In order to evaluate robustness of developed models cross-validation technique is implemented on experimental data. To compare the performance of different AI models, four prediction measures were used: Theil's U coefficients (U_Accuracy and U_Quality), Root Mean Square Error (RMSE) and Regression value (R-value). Obtained results show that best performance belongs to RBFNN based model and then ANFIS based model. U coefficients and RMSE values are obtained less than 0.005 and R-Value is become close to one by using RBFNN based model for testing data, which indicates high accuracy prediction performance.

Pressure drops of fresh cemented paste backfills through coupled test loop experiments and machine learning techniques

In this paper, test loop experiments and machine learning techniques were combined to investigate pressure drops of fresh cemented paste backfill (CPB) mixes. The influence of tailings characteristics on CPB pressure drops was studied and Extensive test loop experiments were performed. The complex mapping from tailings characteristics, cement-tailings ratio, inlet velocity and solids content to pressure drop was successfully learned by decision tree regression (DTR) models. In addition, the influence of training set size and maximum tree depth on DTR performance was investigated. The relative importance of predictor variables was discussed and the visualisation for a representative DTR model was provided. Finally, the current research applied two ensemble techniques, namely random forest and gradient boosting regression tree, to increase the predictive performance of the DTR models. The study found that all ensemble techniques outperformed DTR in the pressure drop prediction of CPB.

Multi-objective optimization model predictive dispatch precooling and ceiling fans in office buildings under different summer weather conditions

In summer, a ceiling fan has a tremendous potential to cool rooms, resulting in energy saving and a peak load reduction. During the critical peak pricing (CPP) period of the electricity, one method to attain substantial energy cost saving is to turn off the heating, ventilating and air conditioning (HVAC) system, but this may result in overheating. Fortunately, precooling and ceiling fans can be used to reduce the feeling of hot sensation. Therefore, the best control strategy for an HVAC system and ceiling fans is to maximize energy cost saving without much thermal comfort sacrifice. This paper presents a multi-objective optimization model predictive control (MO-MPC) of precooling temperature setpoint and ceiling fan speed. The proposed MO-MPC integrates an optimization engine, Dakota, and a detailed energy simulation tool, EnergyPlus, to determine the optimal control strategies considering the energy cost and thermal comfort simultaneously. A sub-program of substituting EnergyPlus with decision tree regression models for building climate prediction is deployed. If the calculation time is too long to achieve real-time control, this sub-program can be adopted to accelerate the calculation. In this study, the proposed MO-MPC framework is applied to a small and large building in four different cities with different CPPs. For a summer day, compared with the baseline cases of no precooling and no ceiling fan, the optimal strategies for the small building achieve energy cost savings between 10.3% and 33.3% and a peak power load decrease from 44.0% to 51.9%. Without precooling but with a ceiling fan control, on a particular summer day, for the large building case in Shanghai, 7.4% energy saving is achieved. The optimal control of the precooling temperature and ceiling fans result in a 14.4% energy cost saving. By using the decision tree regression models instead of EnergyPlus, the calculation time of the big office reduces from about 10 hours 40 minutes to 24.4 minutes.

Applying machine learning approaches to analyze the vulnerable road-users' crashes at statewide traffic analysis zones

Introduction: In this paper, we present machine learning techniques to analyze pedestrian and bicycle crash by developing macro-level crash prediction models. Methods: We collected the 2010–2012 Statewide Traffic Analysis Zone (STAZ) level crash data and developed rigorous machine learning approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To our knowledge, this is the first application of DTR models in the burgeoning macro-level traffic safety literature. Results: The DTR models uncovered the most significant predictor variables for both response variables (pedestrian and bicycle crash counts) in terms of three broad categories: traffic, roadway, and socio-demographic characteristics. Additionally, spatial predictor variables of neighboring STAZs were considered along with the targeted STAZ in both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the model comparison results discovered that the prediction accuracy of the spatial DTR model performed better than the aspatial DTR model. Finally, the current research effort contributed to the safety literature by applying some ensemble techniques (i.e. bagging, random forest, and gradient boosting) in order to improve the prediction accuracy of the DTR models (weak learner) for macro-level crash count. The study revealed that all the ensemble techniques performed slightly better than the DTR model and the gradient boosting technique outperformed other competing ensemble techniques in macro-level crash prediction models.

GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion

Circular RNA (circRNA) is a closed-loop structural non-coding RNA molecule which plays a significant role during the gene regulation processes. There are many previous studies shown that circRNAs can be regarded as the sponges of miRNAs. Thus, circRNA is also a key point for disease diagnosing, treating and inferring. However, traditional experimental approaches to verify the associations between the circRNA and disease are time-consuming and money-consuming. There are few computational models to predict potential circRNA-disease associations, which become our motivation to propose a new computational model. In this study, we propose a machine learning based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). The known circRNA-disease associations' data are downloaded from cricR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). The feature vector of each circRNA-disease association pair is composed of four parts, which are the statistics information of different biological networks, the graph theory information of different biological networks, circRNA-disease associations' network information and circRNA nucleotide sequence information, respectively. Therefore, we use those feature vectors to train the gradient boosting decision tree regression model. Then, the leave one out cross validation (LOOCV) is adopted to evaluate the performance of our computational model. As for predicting some common diseases related circRNAs, our method GBDTCDA also obtains the better results. The Area under the ROC Curve (AUC) values of Basal cell carcinoma, Non-small cell lung cancer and cervical cancer are 95.8%, 88.3% and 93.5%, respectively. For further illustrating the performance of GBDTCDA, a case study of breast cancer is also supplemented in this study. Thus, our proposed method GBDTCDA is a powerful tool to predict potential circRNA-disease associations based on experimental results and analyses.

PRM55 - A NEW ANALYSIS METHODOLOGY TO FIND ATYPICAL COST PROFILES IN PATIENTS LIVING WITH HIV FROM A NATIONAL CLAIM INSURANCE DATA (SNIIRAM)

This study is a large-scale extension of a published study on the economic burden of HIV in France that was solely focused on hospital costs. Through the application of a new analytical methodology, the objective is to find the profiles of patients living with HIV (PLHIV) who overexpress the cost of care, using all reimbursement data of the French health insurance (SNIIRAM database). We performed a retrospective cohort study. The SNIIRAM contains individualized data on all reimbursed health expenses for the entire French population. PLHIV were included between 01/01/2013 and 31/12/2013 by the presence of a care related to HIV. Each patient was followed one year after his inclusion. The annual cost per patient was assessed for the economic burden. The search for patient profiles overexpressing these costs is based on an innovative methodology with a machine learning algorithm. A Boosted Decision Tree regression model was customized to match the size and complexity of health data and to find meaningful profiles (a profile is a combination of dozens of variables). A medical reading of the data identified tens of variables of interest to describe the 98 000 PLHIV included in the study: sociodemographic information, hospital admissions, presence of opportunistic infections, comorbidities, frequency of viral load testing, consultations, and the cost of care. The dedicated machine learning algorithm identified more than ten relevant profiles. An interactive data-visualization tool allowed the restitution of these complex results in a clear way. This study shows that in-depth uses of large healthcare databases require innovative analysis methods. We combined a standard epidemiological study methodology (cohort definition and monitoring, medical review, economic burden) with an innovative mathematical model (Machine Learning) to obtain accurate results regarding patient profiles. These profiles are usable to objectify action plans to improve care policies.

. Ứng dụng kỹ thuật khai phá dữ liệu đánh giá thích nghi đất đai cây cao su trên địa bàn huyện Phú Giáo, tỉnh Bình Dương

This study is aimed to evaluate the applicability of data mining technique by using decision tree in land evaluation. It can be used to determine the land characteristic factors affecting the agricultural land-use potential and quantify the relationship between land characteristic factors and plant productivity in order to improve land evaluation methods that support the foundation of land use planning. Regression decision tree model in this study includes two kinds of variables. The target variable is the productivity (t/ha) and the predictor variables consist of soil types, soil depth, slope, irrigation and texture. The analytical result of survey data shows several factor combinations according to plant average productivity. Based on productivity can evaluate the adaptation level for each correlative factor combination. This study is applied for rubber trees and conducted in Phu Giao district, Binh Duong province. The study shows that the interpretation level of the predictive variables is 96.49%. The area of highly suitable (S1) is 474.67 hectares, suitable (S2) is 53,597.70 hectares. This result is different from the Analytic Hierarchy Process (AHP) method.

Designing erosion management plans in Lebanon using remote sensing, GIS and decision-tree modeling

Soil erosion by water represents a serious threat to the natural and human environment in Mediterranean countries, including Lebanon, which represents a good case study. This research deals with how to use Geographic Information Systems (GIS), remote sensing, and, more specifically, structural classification techniques and decision-tree modeling to map erosion risks and design priority management planning over a representative region of Lebanon. The structural classification organization and analysis of spatial structures (OASIS) of Landsat TM satellite imagery (30 m) was used to define landscapes that prevail in this area and their boundaries, depending on their spectral appearance. The landscape map produced was overlaid sequentially with thematic erosion factorial maps (i.e., slope gradient, drainage density, rainfall quantity, vegetal cover, soil infiltration, soil erodibility, rock infiltration and rock movement). The overlay was visual and conditional using three visual interpretation rules (dominance, unimodality and scarcity conservation), and landscape properties were produced. Rills and gullies were measured in the field, and a decision-tree regression model was developed on the landscapes to statistically explain gully occurrence. This model explained 88% of the variability in field gully measurements. The erosion risk map produced corresponds well to field observations (accuracy of 82%). The landscapes were prioritized according to anti-erosive remedial measures: preventive (Pre), protective (Pro), and restorative (Res). This approach seems useful in Lebanon, but can also serve in other countries with similar geo-environmental conditions or those lacking detailed geospatial data.