Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming
Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied and Environmental Soil Science |
| Online Access: | http://dx.doi.org/10.1155/2021/2630123 |
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| author | Kennedy C. Onyelowe Ahmed M. Ebid Light Nwobia |
| author_facet | Kennedy C. Onyelowe Ahmed M. Ebid Light Nwobia |
| author_sort | Kennedy C. Onyelowe |
| collection | DOAJ |
| description | Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. Three evolutionary trials were executed, and three models were presented considering different permutations of the predictors. The performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value. |
| format | Article |
| id | doaj-art-97e4a12bb1d245a894948b9968ae89c7 |
| institution | Kabale University |
| issn | 1687-7667 1687-7675 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied and Environmental Soil Science |
| spelling | doaj-art-97e4a12bb1d245a894948b9968ae89c72025-02-03T01:24:48ZengWileyApplied and Environmental Soil Science1687-76671687-76752021-01-01202110.1155/2021/26301232630123Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic ProgrammingKennedy C. Onyelowe0Ahmed M. Ebid1Light Nwobia2Department of Civil and Mechanical Engineering, Kampala International University, Kampala, UgandaDepartment of Structural Engineering, Faculty of Engineering and Technology, Future University, New Cairo, EgyptDepartment of Civil Engineering, Michael Okpara University of Agriculture, Umudike, NigeriaVarious environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. Three evolutionary trials were executed, and three models were presented considering different permutations of the predictors. The performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.http://dx.doi.org/10.1155/2021/2630123 |
| spellingShingle | Kennedy C. Onyelowe Ahmed M. Ebid Light Nwobia Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming Applied and Environmental Soil Science |
| title | Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming |
| title_full | Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming |
| title_fullStr | Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming |
| title_full_unstemmed | Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming |
| title_short | Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming |
| title_sort | evolutionary prediction of soil loss from observed rainstorm parameters in an erosion watershed using genetic programming |
| url | http://dx.doi.org/10.1155/2021/2630123 |
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