Rainfall–runoff modeling using an Adaptive Neuro-Fuzzy Inference System considering soil moisture for the Damanganga basin

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS

ACS Applied Bio Materials Pub Date : 2024-04-06 DOI:10.2166/wcc.2024.143

Vrushti C. Kantharia, D. Mehta, Vijendra Kumar, Mohamedmaroof P. Shaikh, Shivendra Jha

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引用次数: 0

Abstract

Rainfall is the major component of the hydrologic cycle and it is the primary source of runoff. The main purpose of this study was to estimate daily discharge by employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) model using rainfall and soil moisture data at three different depths (5 cm, 100 cm and bedrock) for the Damanganga basin. The length of the data for the study period 1983–2022 is 39 years. The model employed nine membership functions for each variable of soil moisture, rainfall, discharge and 30 rules were optimized. The results were compared considering a range of model performance indicators as correlation coefficient (R2) and Nash–Sutcliffe efficiency (NSE) coefficient. The model application results shows that soil moisture at bedrock gives more precise value of daily discharge with (R2) and NSE value as 0.9936 and 0.9981, respectively, as compared to the soil moisture at a depth of 5 and 100 cm. The better results obtained for the measurement of soil moisture in the deeper soil layer are consistent with the hydrological behavior anticipated for the analyzed catchment, where the root-zone soil layer is the driver of the runoff response rather than the surface observations. This study can be helpful to hydrologists in selecting appropriate rainfall–runoff models.

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利用自适应神经模糊推理系统为达曼加盆地建立考虑土壤湿度的降雨-径流模型

降雨是水文循环的主要组成部分，也是径流的主要来源。本研究的主要目的是使用自适应神经模糊推理系统（ANFIS）模型，利用达曼甘流域三个不同深度（5 厘米、100 厘米和基岩）的降雨和土壤水分数据估算日排水量。研究时段为 1983-2022 年，数据长度为 39 年。该模型针对土壤水分、降雨量、排水量等每个变量采用了 9 个成员函数，并优化了 30 条规则。根据相关系数（R2）和纳什-苏克里夫效率（NSE）系数等一系列模型性能指标对结果进行了比较。模型应用结果表明，与 5 厘米和 100 厘米深度的土壤湿度相比，基岩处的土壤湿度能提供更精确的日排水量值，相关系数（R2）和 NSE 值分别为 0.9936 和 0.9981。测量较深土层土壤水分所获得的较好结果与所分析集水区的预期水文行为一致，即根区土层是径流响应的驱动因素，而不是地表观测数据。这项研究有助于水文学家选择合适的降雨-径流模型。

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

ACS Applied Bio Materials Chemistry-Chemistry (all)

CiteScore

9.40

自引率

2.10%

发文量

464

期刊介绍： ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.