نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران
2 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران
3 گروه علوم و مهندسی خاک، دانشگده کشاورزی، دانشگاه تبریز، تبریز، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
In order to effectively manage soil and water resources, it is imperative to investigate wet aggregate stability (WAS) as a fundamental indicator for assessing soil structure and quality. In this study, machine learning techniques, specifically random forest (RF) and random forest optimized with genetic algorithm (GA-RF), were employed. The analysis focused on determining the texture, organic matter content, and lime characteristics of 55 soil samples collected from the Arsbaran forests. Utilizing various input combinations based on correlations with WAS, modeling was performed across seven distinct scenarios. Furthermore, three performance metrics including correlation coefficient (CC), normalized root mean square error (NRMSE), and Wilmot coefficient (WI) were utilized to evaluate the effectiveness of the models. The findings indicated that the RF5 model exhibited superior performance among the random forest models, achieving NRMSE = 0.038, CC = 0.736, and WI = 0.789. Similarly, the GA-RF5 model, optimized through a genetic algorithm approach, demonstrated exceptional performance with NRMSE = 0.031, CC = 0.800, and WI = 0.842 when considering input percentages of sand, silt, and clay. Moreover, results from RF1 (NRMSE = 0.047, CC = 0.589, WI = 0.721) and GA-RF1 (NRMSE = 0.036, CC = 0.662, WI = 0.797) emphasized that clay content exhibited the strongest correlation with stability. Additionally, the incorporation of calcium carbonate equivalent in scenario 7 significantly enhanced model performance and positively influenced the prediction of wet aggregate stability. In summary, the hybrid model combining random forest with a genetic algorithm is recommended for precise and reliable determination of wet aggregate stability in studies focusing on soil properties.
کلیدواژه‌ها [English]
Wet aggregate stability modeling based on random forest optimized with genetic algorithm
EXTENDED ABSTRACT
In order to effectively manage soil and water resources, it is imperative to investigate wet aggregate stability (WAS) as a fundamental indicator for assessing soil structure and quality. Given the labor-intensive and expensive nature of determining WAS values through traditional laboratory techniques, there is a clear advantage in indirectly predicting them using readily available data. Machine learning (ML) techniques present a viable alternative for this purpose. The efficacy of ML stems from its capacity to analyze data on a large scale, enabling the resolution of challenges that conventional linear methods struggle to address economically and satisfactorily. The primary objective of this study is to develop a predictive model for WAS utilizing ML, specifically the random forest (RF) method in standalone mode, and its hybrid with a genetic algorithm (GA-RF) to optimize RF parameters. This unique approach distinguishes the research in the domain of WAS prediction.
The study area selected for investigation was a portion of forested land within the Arsbaran region. A total of 55 soil samples were collected from diverse environmental conditions and subsequently analyzed in the laboratory to determine soil texture, organic matter content, and calcium carbonate equivalent levels. Wet aggregate stability, as assessed by the Kemper and Rosenau test, served as the basis for calibrating machine learning (ML) models. Seven scenarios were explored for predicting wet aggregate stability using soil characteristics through the application of the random forest method in standalone mode and with optimization through a genetic algorithm. The dataset was partitioned such that 70% of the data was allocated for training the models, while the remaining 30% was reserved for testing. Subsequently, the accuracy of the predictive models was evaluated by calculating error metrics, including normalized root mean square error (NRMSE), correlation coefficient (CC), and Wilmot coefficient (WI).
Upon scrutinizing the correlation coefficients between soil attributes and WAS derived from laboratory analysis, a robust relationship between the selected characteristics and the target variable was evident. Among the various random forest models assessed, the RF5 model exhibited notable performance with NRMSE parameters at 0.038, CC at 0.8, and WI at 0.789. Furthermore, the GA-RF5 model, optimized using a genetic algorithm, surpassed the RF5 model with improved metrics of 0.031 NRMSE, 0.800 CC, and 0.842 WI, showcasing enhanced predictive capabilities for WAS. A comparative analysis between the RF5 and GA-RF5 models revealed that the genetic algorithm significantly enhanced the predictive accuracy of RF by elevating R and WI values by 8% and 6.72%, respectively, while reducing NRMSE by 18.42%. Notably, scenario 5 emerged as the optimal model, predicated on the composition of sand, silt, and clay particles.
The findings from RF1 (NRMSE = 0.047, CC = 0.589, WI = 0.721) and GA-RF1 (NRMSE = 0.036, CC = 0.662, WI = 0.797) underscored the pivotal role of clay content in soil structure and its influence on WAS prediction. Clay content was identified as a critical soil property impacting WAS, as it functions as a binding agent that cohesively holds soil particles together. The clay content in the analyzed soils ranged from 5% to 62.5%. Contrarily, organic matter was found to have no discernible effect on WAS, as indicated by the statistical outcomes of scenario 2 models. Moreover, scenarios 6 and 7 demonstrated a substantial reduction of 10.43% and 10.81% in NRMSE in both standalone and optimized modes, highlighting the beneficial impact of lime in enhancing WAS prediction accuracy.
Wet aggregate stability stands as a fundamental soil attribute crucial in determining soil erodibility and hydraulic characteristics. Understanding the key soil components governing WAS is imperative for preserving soil structure integrity. An innovative approach to quantifying WAS involves utilizing easily accessible soil parameters for predictive modeling. The statistical analysis conducted revealed that the RF5 and GA-RF5 models, incorporating soil texture variables, exhibited superior predictive performance. A comparative assessment between these models highlighted the enhanced predictive capabilities of the GA-RF model in forecasting WAS. Furthermore, scenarios 1 and 3 underscored the pivotal role of clay content in soil composition, encapsulating various soil formation processes and factors. Overall, the utilization of the GA-RF machine learning technique yields satisfactory accuracy in predicting WAS based on soil attributes. Notably, organic matter (OM) was found to have negligible impact on WAS, while the inclusion of lime demonstrated a positive effect on improving WAS prediction accuracy.
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