Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India.
Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, 620015, India.
Environ Monit Assess. 2023 Aug 17;195(9):1041. doi: 10.1007/s10661-023-11649-0.
The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are widely employed as valuable tools in watershed management. For this specific study, two modelling approaches were employed: the Soil and Water Assessment Tool (SWAT) model and a set of eight artificial intelligence (AI) models. The AI models consisted of seven data-driven approaches, namely k-nearest neighbour regression, support vector regression, linear regression, artificial neural networks, random forest regression, XGBoost, and Histogram-based Gradient Boost regression. Additionally, a deep learning model known as Long Short-Term Memory (LSTM) was also utilized. The study focused on monthly streamflow modelling in the Murredu River basin, with a calibration period from 1999 to 2003 and a validation period from 2004 to 2005, spanning a total of 7 years from 1999 to 2005. The results indicated that all nine models were generally suitable for simulating the rainfall-runoff process, with the LSTM model demonstrating exceptional performance in both the calibration (R is 0.97 and NSE is 0.96) and validation (R is 0.97 and NSE is 0.92) periods. Its high coefficient of determination (R) and Nash-Sutcliffe efficiency (NSE) values indicated its superior ability to accurately model the rainfall-runoff relationship. While the other models also produced satisfactory results, the findings suggest that selecting the most efficient model, such as the LSTM model, could significantly contribute to the effective management and planning of sustainable water resources in the Murredu watershed.
随着人口增长和工业化等因素引起的供水问题日益严重，人们越来越关注如何在河流流域层面上准确估计径流量。为实现这一目标，降雨-径流模型作为流域管理中的重要工具得到了广泛应用。在这项研究中，采用了两种建模方法：土壤和水评估工具 (SWAT) 模型和一组 8 种人工智能 (AI) 模型。AI 模型包括 7 种数据驱动方法，分别是 K 最近邻回归、支持向量回归、线性回归、人工神经网络、随机森林回归、XGBoost 和基于直方图的梯度提升回归。此外，还使用了一种称为长短期记忆 (LSTM) 的深度学习模型。本研究专注于 Murredu 河流域的月径流量建模，校准期为 1999 年至 2003 年，验证期为 2004 年至 2005 年，总共有 7 年（1999 年至 2005 年）的数据。结果表明，所有 9 种模型总体上都适合模拟降雨-径流过程，LSTM 模型在校准期（R 值为 0.97，NSE 值为 0.96）和验证期（R 值为 0.97，NSE 值为 0.92）都表现出卓越的性能。其高决定系数 (R) 和纳什-苏特克里夫效率 (NSE) 值表明它具有准确模拟降雨-径流关系的卓越能力。虽然其他模型也取得了令人满意的结果，但研究结果表明，选择最有效的模型（如 LSTM 模型）可以为 Murredu 流域的可持续水资源的有效管理和规划做出重大贡献。
