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

Machine Learning in Agriculture: A Comprehensive Updated Review DOI Creative Commons
Lefteris Benos, Aristotelis C. Tagarakis,

Georgios Dolias

et al.

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

Citations

526
Precision Irrigation Management Using Machine Learning and Digital Farming Solutions DOI Creative Commons
Abiodun Emmanuel Abioye, Oliver Hensel, Travis J. Esau

et al.

AgriEngineering, Journal Year: 2022, Volume and Issue: 4(1), P. 70 - 103

Published: Feb. 1, 2022

Freshwater is essential for irrigation and the supply of nutrients plant growth, in order to compensate inadequacies rainfall. Agricultural activities utilize around 70% available freshwater. This underscores importance responsible management, using smart agricultural water technologies. The focus this paper investigate research regarding integration different machine learning models that can provide optimal decision management. article reviews trend applicability techniques, as well deployment developed use by farmers toward sustainable It further discusses how digital farming solutions, such mobile web frameworks, enable management processes, with aim reducing stress faced researchers due opportunity remote monitoring control. challenges, future direction research, are also discussed.

Language: Английский

Citations

151
Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives DOI
Yassine Himeur, Bhagawat Rimal, Abhishek Tiwary

et al.

Information Fusion, Journal Year: 2022, Volume and Issue: 86-87, P. 44 - 75

Published: June 25, 2022

Language: Английский

Citations

138
Soil water erosion susceptibility assessment using deep learning algorithms DOI Creative Commons
Khabat Khosravi, Fatemeh Rezaie, James R. Cooper

et al.

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 618, P. 129229 - 129229

Published: Feb. 6, 2023

Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land degradation and loss, mitigating the negative impacts on ecosystem services, quality, flooding infrastructure. Deep learning algorithms have been gaining attention in geoscience due to their high performance flexibility. However, an understanding potential these provide fast, cheap, accurate predictions lacking. This study provides first quantification this potential. Spatial are made using three deep – Convolutional Neural Network (CNN), Recurrent (RNN) Long-Short Term Memory (LSTM) Iranian catchment that has historically experienced severe erosion. Through a comparison predictive analysis driving geo-environmental factors, results reveal: (1) elevation was most effective variable SWE susceptibility; (2) all developed models had good prediction performance, with RNN being marginally superior; (3) maps revealed almost 40 % highly or very susceptible 20 moderately susceptible, indicating need control catchment. algorithms, catchments can potentially be predicted accurately ease readily available data. Thus, reveal great use data poor catchments, such as one studied here, especially developing nations where technical modeling skills processes occurring may

Language: Английский

Citations

97
Prediction model for rice husk ash concrete using AI approach: Boosting and bagging algorithms DOI
Muhammad Nasir Amin, Bawar Iftikhar,

Kaffayatullah Khan

et al.

Structures, Journal Year: 2023, Volume and Issue: 50, P. 745 - 757

Published: Feb. 22, 2023

Language: Английский

Citations

87
Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins DOI

Amirhosein Mosavi,

Mohammad Golshan, Saeid Janizadeh

et al.

Geocarto International, Journal Year: 2020, Volume and Issue: 37(9), P. 2541 - 2560

Published: Sept. 28, 2020

The mountainous watersheds are increasingly challenged with extreme erosions and devastating floods due to climate change human interventions. Hazard mapping is essential for local policymaking prevention, planning the mitigation actions, also adaptation extremes. This study proposes novel predictive models susceptibility flood erosion. Furthermore, this elaborates on prioritizing existing sub-basins in terms of erosion susceptibility. A comparative analysis generalized linear model (GLM), flexible discriminate analyses (FDA), multivariate adaptive regression spline (MARS), random forest (RF), their ensemble performed ensure highest performance. priority sensitivity was determined based best model. results showed that GLM, FDA, MARS, RF, had an area under curve (AUC) 0.91, 0.92, 0.89, 0.93 0.94, respectively, modeling Also, AUC 0.93, 0.96, 0.97, determining Priority assessment model, approach, indicated SW3 SW5 were found have high soil erosion, respectively.

Language: Английский

Citations

113
Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture DOI Open Access
Ania Cravero, Samuel Sepúlveda

Electronics, Journal Year: 2021, Volume and Issue: 10(5), P. 552 - 552

Published: Feb. 26, 2021

The data generated in modern agricultural operations are provided by diverse elements, which allow a better understanding of the dynamic conditions crop, soil and climate, indicates that these processes will be increasingly data-driven. Big Data Machine Learning (ML) have emerged as high-performance computing technologies to create new opportunities unravel, quantify understand through data. However, there many challenges achieve integration technologies. It implies making some adaptations ML for using it with Data. These must consider increasing volume data, its variety transmission speed issues. This paper provides information on use agriculture, identifying challenges, design architectures systems. We conducted Systematic Literature Review (SLR), allowed us analyze 34 real cases applied agriculture. review may interest computer or scientists electronic software engineers. results show manipulating large volumes is no longer challenge due Cloud There still regarding (1) processing little control different stages, raw, semi-processed processed (value data); (2) visualization systems, support technical understood farmers.

Language: Английский

Citations

90
Susceptibility mapping of groundwater salinity using machine learning models DOI

Amirhosein Mosavi,

Farzaneh Sajedi Hosseini, Bahram Choubin

et al.

Environmental Science and Pollution Research, Journal Year: 2020, Volume and Issue: 28(9), P. 10804 - 10817

Published: Oct. 25, 2020

Language: Английский

Citations

72
A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction DOI
Sumudu Senanayake, Biswajeet Pradhan, Abdullah Alamri

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 845, P. 157220 - 157220

Published: July 12, 2022

Language: Английский

Citations

58
Data-driven prediction of neutralizer pH and valve position towards precise control of chemical dosage in a wastewater treatment plant DOI
Yanran Xu, Xuhui Zeng,

Sandy Bernard

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 348, P. 131360 - 131360

Published: March 16, 2022

Language: Английский

Citations

52