A comparison of the performance of SWAT and artificial intelligence models for monthly rainfall–runoff analysis in the Peddavagu River Basin, India

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Padala Raja Shekar, Aneesh Mathew, A. Pandey, Avadhoot Bhosale
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引用次数: 1

Abstract

Rainfall–runoff (R–R) analysis is essential for sustainable water resource management. In the present study focusing on the Peddavagu River Basin, various modelling approaches were explored, including the widely used Soil and Water Assessment Tool (SWAT) model, as well as seven artificial intelligence (AI) models. The AI models consisted of six data-driven models, namely support vector regression, artificial neural network, multiple linear regression, Extreme Gradient Boosting (XGBoost) regression, k-nearest neighbour regression, and random forest regression, along with one deep learning model called long short-term memory (LSTM). To evaluate the performance of these models, a calibration period from 1990 to 2005 and a validation period from 2006 to 2010 were considered. The evaluation metrics used were R2 (coefficient of determination) and NSE (Nash–Sutcliffe Efficiency). The study's findings revealed that all eight models yielded generally acceptable results for modelling the R–R process in the Peddavagu River Basin. Specifically, the LSTM demonstrated very good performance in simulating R–R during both the calibration period (R2 is 0.88 and NSE is 0.88) and the validation period (R2 is 0.88 and NSE is 0.85). In conclusion, the study highlighted the growing trend of adopting AI techniques, particularly the LSTM model, for R–R analysis.
SWAT与人工智能模型在印度佩德瓦古河流域月降雨径流分析中的性能比较
降雨径流(R-R)分析对可持续水资源管理至关重要。本研究以佩德瓦古河流域为研究对象,探索了多种建模方法,包括广泛使用的水土评估工具(SWAT)模型,以及7种人工智能(AI)模型。人工智能模型包括6个数据驱动模型,即支持向量回归、人工神经网络、多元线性回归、极端梯度增强(XGBoost)回归、k近邻回归和随机森林回归,以及一个名为长短期记忆(LSTM)的深度学习模型。为了评估这些模型的性能,我们考虑了1990 - 2005年的校准期和2006 - 2010年的验证期。使用的评价指标为R2(决定系数)和NSE(纳什-萨克利夫效率)。该研究的结果表明,所有8个模型在模拟佩德瓦古河流域的R-R过程时都产生了普遍可接受的结果。具体而言,LSTM在校正期(R2为0.88,NSE为0.88)和验证期(R2为0.88,NSE为0.85)均表现出非常好的R-R模拟性能。总之,该研究强调了采用人工智能技术,特别是LSTM模型进行R-R分析的日益增长的趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
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