ارزیابی جامع ریسک شوری آبخوان سرخون با بهره‌گیری از ترکیب مدل‌های یادگیری ماشین

نوع مقاله : پژوهشی

نویسندگان

1 استادیار، گروه علوم و مهندسی آب، مجتمع آموزش عالی میناب، دانشگاه هرمزگان، بندرعباس

2 استادیار، گروه مهندسی منابع طبیعی، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس

3 استادیار، مرکز مطالعات و تحقیقات (پژوهشکده) هرمز، دانشگاه هرمزگان، بندرعباس

چکیده

ارزیابی ریسک شوری آبخوان به‏خصوص در مناطق نزدیک ساحل اهمیت زیادی دارد. در پژوهش حاضر تلاش شد از طریق ترکیب مدل‏ پتانسیل آسیب‏پذیری آبخوان و الگوریتم‏های یادگیری ماشین، چارچوب جامعی برای ارزیابی ریسک شوری در آبخوان سرخون واقع در استان هرمزگان ایجاد شود. در مرحلۀ نخست لایه‏های ورودی مورد نیاز برای تولید نقشۀ پتانسیل آسیب‏پذیری آبخوان براساس مدل دراستیک تهیه و ترکیب شد. سپس، با استفاده از سه الگوریتم یادگیری ماشین شامل جنگل تصادفی، افزایش گرادیان اکسترمم (XGBoost) و درختان رگرسیون جمع‏شدۀ بیزی (BART) و با استفاده از 12 فاکتور تأثیرگذار روی آب زیرزمینی از جمله رطوبت توپوگرافیک، خاک، پوشش گیاهی و عوامل دیگر، نقشۀ احتمال خطر شور شدن تهیه شد. قبل از مدل‏سازی آزمون هم‏خطی روی داده‏ها انجام شد و مشاهده شد که هم‏خطی در بین پارامترهای ورودی مدل‏ها وجود ندارد. ارزیابی کارایی مدل‏سازی با منحنی ویژگی عملگر نسبی ROC)) نشان داد هر سه الگوریتم دقت بسیار خوب و سطح زیرمنحنی AUC)) بیش از 90 درصد دارند. بنابراین، هر سه مدل بر اساس میزان سطح زیرمنحنی خود ترکیب شدند تا یک نقشۀ واحد برای احتمال وقوع خطر شوری به دست آید. در انتها، نقشۀ ریسک شوری براساس مقادیر آسیب‏پذیری، شوری و احتمال وقوع خطر تهیه شد. نقشۀ ریسک به‌دست‏آمده نشان داد قسمت‏های شرقی آبخوان ریسک شوری بسیار زیاد دارد که علت این امر تمرکز زیاد زمین‏های کشاورزی در این بخش دشت است. نتایج پژوهش حاضر نشان داد دستیابی به یک نقشۀ‏ قابل اتکا برای ارزیابی ریسک شوری آبخوان به وسیلۀ ترکیب مدل‏های یادگیری ماشین و مدل‏های آسیب‏پذیری آبخوان امکان‏پذیر است.

کلیدواژه‌ها


عنوان مقاله [English]

Comprehensive Risk Assessment of Sarkhoon Aquifer Salinization Using a Combination of Machine Learning Models

نویسندگان [English]

  • Fariborz Mohammadi 1
  • Ali Nafarzadegan 2
  • Mohamad Kazemi 3
1 Assistant Professor, Department of Water Sciences & Engineering, Minab Higher Education Center, University of Hormozgan, Bandar Abbas, Iran
2 Assistant Professor, Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran
3 Assistant Professor, Hormoz Study and Research Center, University of Hormozgan, Bandar Abbas, Iran
چکیده [English]

Risk assessment of aquifer salinization is of great importance especially in regions near the coast. In this study, it was attempted to develop a comprehensive framework for salinity risk assessment for Sarkhoon aquifer, Hormozgan province by combining the aquifer vulnerability potential model and machine learning algorithms. In the first step, the input layers required for the generation of the aquifer vulnerability potential map were prepared based on DRASTIC model and combined. Then, the map of salinization hazard occurrence probability was obtained by using three machine learning models of Random Forest, Extreme Gradient Boosting (XGBoost), and Bayesian Additive Regression Trees (BART) by considering 12 factors affecting groundwater including topographic wetness, soil, vegetation and other factors. Prior to modeling, a collinearity test was performed on the data and it was observed that there was no collinearity between the models’ input parameters. Evaluation of the modeling performance with the receiver operating characteristic (ROC) curve indicated that all three algorithms had very good accuracies with area under curve (AUC) values higher than 90%. Thus, all three models were combined based on their AUC values to produce a united map for the probability of salinization hazard occurrence. Finally, the map of salinization risk was generated based on the values for the vulnerability, salinity and hazard occurrence probability. The obtained risk map showed that the eastern part of the aquifer has very high salinization risk which is due to the high concentration of agricultural land in this part of the plain. The results of this study revealed that achieving a reliable map for assessing aquifer salinization risk is possible by combining machine learning models.

کلیدواژه‌ها [English]

  • Groundwater
  • Salinization hazard
  • Random forest
  • DRASTIC index
  • Vulnerability potential
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