Modeling Multi-Event Non-Point Source Pollution in a Data-Scarce Catchment Using ANN and Entropy Analysis

نویسندگان

  • Lei Chen
  • Cheng Sun
  • Guobo Wang
  • Hui Xie
  • Zhenyao Shen
چکیده

Event-based runoff–pollutant relationships have been the key for water quality management, but the scarcity of measured data results in poor model performance, especially for multiple rainfall events. In this study, a new framework was proposed for event-based non-point source (NPS) prediction and evaluation. The artificial neural network (ANN) was used to extend the runoff–pollutant relationship from complete data events to other data-scarce events. The interpolation method was then used to solve the problem of tail deviation in the simulated pollutographs. In addition, the entropy method was utilized to train the ANN for comprehensive evaluations. A case study was performed in the Three Gorges Reservoir Region, China. Results showed that the ANN performed well in the NPS simulation, especially for light rainfall events, and the phosphorus predictions were always more accurate than the nitrogen predictions under scarce data conditions. In addition, peak pollutant data scarcity had a significant impact on the model performance. Furthermore, these traditional indicators would lead to certain information loss during the model evaluation, but the entropy weighting method could provide a more accurate model evaluation. These results would be valuable for monitoring schemes and the quantitation of event-based NPS pollution, especially in data-poor catchments.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Modeling of Non-Point Source Pollution by Long-Term Hydrologic Impact Assessment (L-THIA) (Case Study: Zayandehrood Watershed) in 2015‎

Background & Aims of the Study: In this research, Long-Term Hydrologic Impact Assessment model is selected for simulation of runoff and NPS pollution. The aim of this study is modeling of non-point source pollution by L-THIA model in Zayandehrood watershed in 2015. Materials & Methods: In this study, analytical survey and investigation of references in the context of libr...

متن کامل

مدل سازی آلودگی غیرنقطه ای با استفاده از سیستم اطلاعات جغرافیایی (GIS) برای ارائه بهترین شیوه های مدیریت (BMP) در حوضه آبخیز گرگانرود

The most important pollutants that cause water pollution are nitrogen and phosphorus from agricultural runoff called Non-Point Source Pollution (NPS). To solve this problem, management practices known as BMPs or Best Management Practices are applied. One of the common methods for Non-Point Source Pollution prediction is modeling. By modeling, efficiency of many practices can be tested before ap...

متن کامل

بررسی کاربرد مدل‌های هوش محاسباتی در شبیه سازی و پیش بینی بهنگام جریان‌های سیلابی

The potential of artificial neural network models for simulating the hydrologic behaviour of catchments is presented in this paper. The main purpose is the modeling of river flow in a multi-gauging station catchment and real time prediction of peak flow downstream. The study area covers the Upper Derwent River catchment located in River Trent basin. The river flow has been predicted (at Whatsta...

متن کامل

Dynamic Analysis of Multi-Directional Functionally Graded Panels and Comparative Modeling by ANN

In this paper dynamic analysis of multi-directional functionally graded panel is studied using a semi-analytical numerical method entitled the state-space based differential method (SSDQM) and comparative behavior modeling by artificial neural network (ANN) for different parameters. A semi-analytical approach which makes use the three-dimensional elastic theory and assuming the material propert...

متن کامل

Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer....

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Entropy

دوره 19  شماره 

صفحات  -

تاریخ انتشار 2017