Spatial variability of soil water erosion: Comparing empirical and intelligent techniques DOI Creative Commons
Ali Golkarian, Khabat Khosravi, Mahdi Panahi

et al.

Geoscience Frontiers, Journal Year: 2022, Volume and Issue: 14(1), P. 101456 - 101456

Published: Aug. 22, 2022

Soil water erosion (SWE) is an important global hazard that affects food availability through soil degradation, a reduction in crop yield, and agricultural land abandonment. A map of susceptibility first vital step management conservation. Several machine learning (ML) algorithms optimized using the Grey Wolf Optimizer (GWO) metaheuristic algorithm can be used to accurately SWE susceptibility. These include Convolutional Neural Networks (CNN CNN-GWO), Support Vector Machine (SVM SVM-GWO), Group Method Data Handling (GMDH GMDH-GWO). Results obtained these compared with well-known Revised Universal Loss Equation (RUSLE) empirical model Extreme Gradient Boosting (XGBoost) ML tree-based models. We apply methods together frequency ratio (FR) Information Gain Ratio (IGR) determine relationship between historical data controlling geo-environmental factors at 116 sites Noor-Rood watershed northern Iran. Fourteen are classified topographical, hydro-climatic, cover, geological groups. next divided into two datasets, one for training (70% samples = 81 locations) other validation (30% 35 locations). Finally model-generated maps were evaluated Area under Receiver Operating Characteristic (AU-ROC) curve. Our results show elevation rainfall erosivity have greatest influence on SWE, while texture hydrology less important. The CNN-GWO (AU-ROC 0.85) outperformed models, specifically, order, SVR-GWO GMDH-GWO (AUC 0.82), CNN GMDH 0.81), SVR XGBoost 0.80), RULSE. Based RUSLE model, loss ranges from 0 2644 t ha–1yr−1.

Language: Английский

Modelling and mapping of soil erosion susceptibility using machine learning in a tropical hot sub-humid environment DOI
Rakhohori Bag, Ismail Mondal,

Mahroo Dehbozorgi

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 364, P. 132428 - 132428

Published: June 3, 2022

Language: Английский

Citations

46
Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review DOI Creative Commons
Olusegun Folorunso, Oluwafolake Ojo, Mutiu Abolanle Busari

et al.

Big Data and Cognitive Computing, Journal Year: 2023, Volume and Issue: 7(2), P. 113 - 113

Published: June 8, 2023

Agriculture is essential to a flourishing economy. Although soil for sustainable food production, its quality can decline as cultivation becomes more intensive and demand increases. The importance of healthy cannot be overstated, lack nutrients significantly lower crop yield. Smart prediction digital mapping offer accurate data on nutrient distribution needed precision agriculture. Machine learning techniques are now driving intelligent systems. This article provides comprehensive analysis the use machine in predicting qualities. components qualities soil, parameters, existing dataset, map, effect growth, well information system, key subjects under inquiry. agriculture, exemplified by this study, improve productivity.

Language: Английский

Citations

40
Prediction of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious material (SCM): Optimization and hyper-tuning DOI Creative Commons
Muhammad Nasir Amin,

Kaffayatullah Khan,

Abdullah Mohammad Abu Arab

et al.

Journal of Materials Research and Technology, Journal Year: 2023, Volume and Issue: 25, P. 1495 - 1536

Published: June 6, 2023

Rice Husk ash (RHA) utilization in concrete as a waste material can contribute to the formation of robust cementitious matrix with utmost properties. The strength HPC when subjected compression test is determined by combination and quantity materials used its production. Thus, making mixed design process challenging ambiguous. objective this research forecast containing RHA, using diverse range machine learning techniques, including both individual ensemble learners such bagging boosting. This study will cause significant implications for sustainable construction practices facilitating development an efficient effective method forecasting HPC. Individual (ML) algorithms are incorporated methods bagging, adaptive boosting, random forest algorithms. These techniques use create twenty different sub-models. Afterward, these sub-models train optimized achieving best possible value R2. were further fine-tuned obtain In order assess or evaluate quality, reliability, generalizability data, K-Fold cross-validation utilized. Furthermore, index measuring statistical performance models validate compare assessment models. findings indicate that boosting enhances prediction accuracy weak models, minimum errors R2 > 0.92 achieved decision tree forest. general, model learner (ML).

Language: Английский

Citations

39
A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin DOI
Muhammad Iftikhar Faraz, Siyab Ul Arifeen, Muhammad Nasir Amin

et al.

Structures, Journal Year: 2023, Volume and Issue: 53, P. 937 - 948

Published: May 7, 2023

Language: Английский

Citations

24
Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature DOI Creative Commons
Abdulaziz Alaskar, Ghasan Alfalah, Fadi Althoey

et al.

Case Studies in Construction Materials, Journal Year: 2023, Volume and Issue: 18, P. e02199 - e02199

Published: June 6, 2023

The elevated temperature severely influences the mixed properties of concrete, causing a decrease in its strength properties. Accurate proportioning concrete components for obtaining required compressive (C-S) at temperatures is complicated and time-taking process. However, using evolutionary programming techniques such as gene expression (GEP) multi-expression (MEP) provides accurate prediction C-S. This article presents genetic programming-based models (such (MEP)) forecasting temperatures. In this regard, 207 C-S values were obtained from previous studies. model's development, was considered output parameter with nine most influential input parameters, including; Nano silica, cement, fly ash, water, temperature, silica fume, superplasticizer, sand, gravels. efficacy accuracy GEP MEP-based assessed by statistical measures mean absolute error (MAE), correlation coefficient (R2), root square (RMSE). Moreover, also evaluated external validation different criteria recommended comparing MEP models, gave higher R2 lower RMSE MAE 0.854, 5.331 MPa, 0.018 MPa respectively, indicating strong between actual anticipated outputs. Thus, GEP-based model used further sensitivity analysis, which revealed that cement influencing factor. addition, proposed simple mathematical can be easily implemented practice.

Language: Английский

Citations

23
Prediction of compressive strength of glass powder concrete based on artificial intelligence DOI
Xu Miao, Bingcheng Chen, Yuxi Zhao

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 91, P. 109377 - 109377

Published: April 30, 2024

Language: Английский

Citations

14
Modelling of soil erosion susceptibility incorporating sediment connectivity and export at landscape scale using integrated machine learning, InVEST-SDR and Fragstats DOI
Raj Kumar Bhattacharya, Nilanjana Das Chatterjee, Kousik Das

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 353, P. 120164 - 120164

Published: Jan. 31, 2024

Language: Английский

Citations

13
Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development DOI Open Access
Seyed Mostafa Biazar, Golmar Golmohammadi,

Rohit R. Nedhunuri

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 2250 - 2250

Published: March 5, 2025

Hydrology relates to many complex challenges due climate variability, limited resources, and especially, increased demands on sustainable management of water soil. Conventional approaches often cannot respond the integrated complexity continuous change inherent in system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing most important facets hydrological research, including soil land surface modeling, streamflow, groundwater forecasting, quality assessment, remote sensing applications resources. In AI techniques could further enhance accuracy texture analysis, moisture estimation, erosion prediction for better management. Advanced models also be used as a tool forecast streamflow levels, therefore providing valuable lead times flood preparedness resource planning transboundary basins. quality, AI-driven methods improve contamination risk enable detection anomalies, track pollutants assist treatment processes regulatory practices. combined with open new perspectives monitoring resources at spatial scale, from forecasting storage variations. paper’s synthesis emphasizes AI’s immense potential hydrology; it covers latest advances future prospects field ensure

Language: Английский

Citations

1
Integrating Remote Sensing, GIS, and AI Technologies in Soil Erosion Studies DOI Creative Commons
Salman Selmy, Dmitry E. Kucher, Ali RA Moursy

et al.

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: March 7, 2025

Soils are one of the most valuable non-renewable natural resources, and conserving them is critical for agricultural development ecological sustainability because they provide numerous ecosystem services. Soil erosion, a complex process caused by forces such as rainfall wind, poses significant challenges to ecosystems, agriculture, infrastructure, water quality, necessitating advanced monitoring modeling techniques. It has become global issue, threatening systems food security result climatic changes human activities. Traditional soil erosion field measurement methods have limitations in spatial temporal coverage. The integration new techniques remote sensing (RS), geographic information (GIS), artificial intelligence (AI) revolutionized our approach understanding managing erosion. RS technologies widely applicable investigations due their high efficiency, time savings, comprehensiveness. In recent years, advancements sensor technology resulted fine spatial-resolution images increased accuracy detection mapping purposes. Satellite imagery provides data on land cover properties, whereas digital elevation models (DEMs) detailed required assess slope flow accumulation, which important factors modeling. GIS enhances analysis integrating multiple datasets, making it easier identify hot spots utilizing like Revised Universal Loss Equation (RUSLE) estimate loss guide management decisions. Furthermore, AI techniques, particularly machine learning (ML) deep (DL), significantly improve predictions analyzing historical extracting relevant features from imagery. These use convolutional neural networks (CNNs) augmentation, well risk factors. Additionally, innovative methods, including biodegradable materials, hydroseeding, autonomous vehicles precision being developed prevent mitigate effectively. Although specific case studies demonstrate successful implementation this integrated framework variety landscapes, ongoing availability model validation must be addressed. Ultimately, collaboration RS, GIS, not only but also paves way effective control strategies, underscoring importance continued research vital area. This chapter addresses basic concerns related application erosion: concepts, acquisition, tools, types, management, visualization, an overview type its role

Language: Английский

Citations

1
Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models DOI Creative Commons

Amirhosein Mosavi,

Farzaneh Sajedi Hosseini, Bahram Choubin

et al.

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 145564 - 145576

Published: Jan. 1, 2020

Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for quality monitoring management. Advancement accurate prediction systems is essential the identification vulnerable areas in order to raise awareness about potential protect top-soil due time. In this study, three machine learning models Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), Bayesian Generalized Linear Model (Bayesglm) developed building their performance evaluated delineation maps. Both natural human effective factors (16 features) were used as predictors modeling randomly divided into training (80%) testing (20%) datasets. The using datasets after calibration selected features by recursive feature elimination (RFE) method. RFE indicated that with 8 had better among 1 16 (Accuracy = 0.87). Results highlighted StoGB a good performance, whereas RotFor Bayesglm an excellent based on Kappa values (>0.85). Although spatial was different, all central parts region have very high which matches agricultural areas, lithology map, locations low depth groundwater, slope, elevation. Additionally, near Maharlu lake decline also located zone, can confirm effects saltwater intrusion. maps produced study utmost importance water security sustainable agriculture.

Language: Английский

Citations

67