Stability prediction for soil-rock mixture slopes based on a novel ensemble learning model DOI Creative Commons

Xiaodi Fu,

Bo Zhang, Linjun Wang

et al.

Frontiers in Earth Science, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 10, 2023

Soil-rock mixtures are geological materials with complex physical and mechanical properties. Therefore, the stability prediction of soil-rock mixture slopes using machine learning methods is an important topic in field engineering. This study uses investigated detail as dataset. An intelligent optimization algorithm-weighted mean vectors algorithm (INFO) coupled a algorithm. One new ensemble models, which named IN-Voting, INFO voting model. Twelve single models sixteen novel IN-Voting built to predict slopes. Then, accuracies above compared evaluated three evaluation metrics: coefficient determination ( R 2 ), square error (MSE), absolute (MAE). Finally, model based on five weak learners used final for predicting also analyze importance input parameters. The results show that: 1) Among 12 slopes, MLP (Multilayer Perceptron) has highest accuracy. 2) higher accuracy than up 0.9846) structural factors affecting decreasing order rock content, bedrock inclination, slope height, angle.

Language: Английский

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions DOI
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 129, P. 107559 - 107559

Published: Dec. 3, 2023

Language: Английский

Citations

61
An integrated approach of remote sensing and geospatial analysis for modeling and predicting the impacts of climate change on food security DOI Creative Commons
Mohammad Kazemi Garajeh, Behnam Salmani,

Saeid Zare Naghadehi

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Jan. 19, 2023

The agriculture sector provides the majority of food supplies, ensures security, and promotes sustainable development. Due to recent climate changes as well trends in human population growth environmental degradation, need for timely agricultural information continues rise. This study analyzes predicts impacts change on security (FS). For 2002-2021, Landsat, MODIS satellite images predisposing variables (land surface temperature (LST), evapotranspiration, precipitation, sunny days, cloud ratio, soil salinity, moisture, groundwater quality, types, digital elevation model, slope, aspect) were used. First, we used a deep learning convolutional neural network (DL-CNN) based Google Earth Engine (GEE) detect land (AL). A remote sensing-based approach combined with analytical process (ANP) model was identify frost-affected areas. We then analyzed relationship between climatic, geospatial, topographical AL found negative correlations - 0.80, 0.58, 0.43, 0.45 LST, respectively. There is positive correlation quality 0.39, 0.25, 0.21, 0.77, areas elevation, aspect are 0.55, 0.40, 0.52, 0.35, 0.45, 0.39. Frost-affected have day, moisture 0.68, 0.23, 0.38, Our findings show that increase salinity associated decrease AL. Additionally, decreases decreasing quality. It also increase, well. Furthermore, when decrease. Finally, predicted FS threat 2030, 2040, 2050, 2060 using CA-Markov method. According results, will by 0.36% from 2030 2060. Between 2060, however, area very high about 10.64%. In sum, this accentuates critical region. proposed methods could be helpful researchers quantify different regions periods.

Language: Английский

Citations

48
Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco DOI Creative Commons
Sliman Hitouri, Meriame Mohajane, Meriam Lahsaini

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(5), P. 858 - 858

Published: Feb. 29, 2024

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images inventory preparation integrated four machine learning models (Random Forest: RF, Classification Regression Trees: CART, Support Vector Machine: SVM, Extreme Gradient Boosting: XGBoost) predict Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power distance from streams, roads, lithology, rainfall, land use/land cover, normalized vegetation index) were as conditioning factors. The dataset was divided into 70% 30% training validation purposes using popular library, scikit-learn (i.e., train_test_split) Python programming language. Additionally, area under curve (AUC) evaluate performance models. accuracy results showed that XGBoost predicted with AUC values 0.807, 0.780, 0.756, 0.727, respectively. However, RF model performed better at prediction compared other applied. As per model, 22.49%, 16.02%, 12.67%, 18.10%, 31.70% watershed are estimated being very low, moderate, high, highly susceptible flooding, Therefore, integration data could have promising predicting similar environments.

Language: Английский

Citations

18
Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS DOI
Hassan Ait Naceur,

Hazem Ghassan Abdo,

Brahim Igmoullan

et al.

Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(3)

Published: Feb. 1, 2024

Language: Английский

Citations

7
Multi-hazards (landslides, floods, and gully erosion) modeling and mapping using machine learning algorithms DOI
Ahmed M. Youssef, Ali M. Mahdi,

Mohamed M. Al-Katheri

et al.

Journal of African Earth Sciences, Journal Year: 2022, Volume and Issue: 197, P. 104788 - 104788

Published: Nov. 9, 2022

Language: Английский

Citations

28
Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high atlas, Morocco DOI Creative Commons

Abdeslam Baiddah,

Samira Krimissa,

Sonia Hajji

et al.

Frontiers in Earth Science, Journal Year: 2023, Volume and Issue: 11

Published: May 30, 2023

Gully erosion has been identified in recent decades as a global threat to people and property. This problem also affects the socioeconomic stability of societies therefore limits their sustainable development, it impacts nonrenewable resource on human scale, namely, soil. The focus this study is evaluate prediction performance four machine learning (ML) models: Logistic Regression (LR), classification regression tree (CART), Linear Discriminate Analysis (LDA), k-Nearest Neighbors (kNN), which are novel approaches gully modeling research, particularly semi-arid regions with mountainous character. 204 samples areas non-erosion were collected through field surveys high-resolution satellite images, 17 significant factors considered. dataset cells (70% for training 30% testing) randomly prepared assess robustness different models. functional relevance between soil effective was computed using ML models evaluated metrics, including accuracy, kappa coefficient. kNN ideal model study. value AUC from ROC considering testing datasets KNN 0.93; remaining associated similar terms values. values GLM, LDA, CART 0.90, 0.91, 0.84, respectively. accuracy validation CART, KNN, GLM 0.85, 0.82, 0.89, 0.84 Kappa 0.70, 0.65, 0.68, models, particular have achieved outstanding results creating susceptibility maps. maps created most reliable could be useful tool management, watershed conservation prevention water losses.

Language: Английский

Citations

14
A comprehensive remote sensing-based Agriculture Drought Condition Indicator (CADCI) using machine learning DOI Creative Commons
Khaled F. Alkaraki, Khaled Hazaymeh

Environmental Challenges, Journal Year: 2023, Volume and Issue: 11, P. 100699 - 100699

Published: Feb. 27, 2023

Agriculture drought is a decrease in soil moisture during growing season. In this study, comprehensive remote sensing-based Drought Condition Indicator (CADCI) was developed to monitor the agriculture semi-arid environments and assess its effectiveness rainfed regions (A) Jordan (B) Syria. First, sensed-based drought-condition spectral indices [i.e., Vegetation Index (VCI), Temperature (TCI), Evapotranspiration (ETCI), Precipitation (PCI), Soil Moisture (SMCI), Health (VHI)] were calculated using data from Moderate Resolution Imaging Spectroradiometer satellite (MODIS) [Land Surface (LST), Normalized Difference (NDVI), evapotranspiration (ET)]; Global Measurement (GPMs); Active Passive (SMAPs); Sentinel-1A. Second, Random Forest (RF) used estimate determine relative importance of these based on Standardized (SPI) values select three that have most monthly short-term identifying for environments, which PCI, TCI, VCI. Third, integrated identify severity specific thresholds compare pixel-specific value with study area average value. For instance, severe condition identified if all indicate condition, moderate or mild conditions are any two one conditions, respectively. Lastly, none condition. Finally, SPI sets 1 3-months (SPI-1 SPI-3) evaluate performance CADCI. The results showed CADCI has high agreement SPI-1 classes areas, overall accuracy Kappa-values 85% 0.80, A 83% 0.76 B, Consequently, shows ability agricultural environments. Perhaps, it could be applicable larger areas due spatial resolution input dataset.

Language: Английский

Citations

12
Examining the role of class imbalance handling strategies in predicting earthquake-induced landslide-prone regions DOI
Quoc Bao Pham, Ömer Ekmekcioğlu, Sk Ajim Ali

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 143, P. 110429 - 110429

Published: May 19, 2023

Language: Английский

Citations

12
Gully erosion mapping susceptibility in a Mediterranean environment: A hybrid decision-making model DOI Creative Commons
Sliman Hitouri, Meriame Mohajane, Sk Ajim Ali

et al.

International Soil and Water Conservation Research, Journal Year: 2023, Volume and Issue: 12(2), P. 279 - 297

Published: Oct. 7, 2023

Gully erosion is one of the main natural hazards, especially in arid and semi-arid regions, destroying ecosystem service human well-being. Thus, gully susceptibility maps (GESM) are urgently needed for identifying priority areas on which appropriate measurements should be considered. Here, we proposed four new hybrid Machine learning models, namely weight evidence -Multilayer Perceptron (MLP- WoE), –K Nearest neighbours (KNN- - Logistic regression (LR- Random Forest (RF- mapping exploring opportunities GIS tools Remote sensing techniques El Ouaar watershed located Souss plain Morocco. Inputs developed models composed dependent (i.e., points) a set independent variables. In this study, total 314 points were randomly split into 70% training stage (220 gullies) 30% validation (94 sets identified study area. 12 conditioning variables including elevation, slope, plane curvature, rainfall, distance to road, stream, fault, TWI, lithology, NDVI, LU/LC used based their importance mapping. We evaluate performance above following statistical metrics: Accuracy, precision, Area under curve (AUC) values receiver operating characteristics (ROC). The results indicate RF- WoE model showed good accuracy with (AUC = 0.8), followed by KNN-WoE 0.796), then MLP-WoE 0.729) LR-WoE 0.655), respectively. provide information valuable tool decision-makers planners identify where urgent interventions applied.

Language: Английский

Citations

12
Spatial mapping of hydrologic soil groups using machine learning in the Mediterranean region DOI
Elhousna Faouzi, Abdelkrim Arioua, Mustapha Namous

et al.

CATENA, Journal Year: 2023, Volume and Issue: 232, P. 107364 - 107364

Published: July 25, 2023

Language: Английский

Citations

11