Hiwa Farajpanah, Arash Adib, Morteza Lotfirad, Hassan Esmaeili-Gisavandani, Mohammad Mehdi Riyahi, Arash Zaerpour
{"title":"波形匹配算法在利用小波-ML 模型改进月径流预报中的新应用","authors":"Hiwa Farajpanah, Arash Adib, Morteza Lotfirad, Hassan Esmaeili-Gisavandani, Mohammad Mehdi Riyahi, Arash Zaerpour","doi":"10.2166/hydro.2024.128","DOIUrl":null,"url":null,"abstract":"\n \n The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.","PeriodicalId":507813,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel application of waveform matching algorithm for improving monthly runoff forecasting using wavelet–ML models\",\"authors\":\"Hiwa Farajpanah, Arash Adib, Morteza Lotfirad, Hassan Esmaeili-Gisavandani, Mohammad Mehdi Riyahi, Arash Zaerpour\",\"doi\":\"10.2166/hydro.2024.128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.\",\"PeriodicalId\":507813,\"journal\":{\"name\":\"Journal of Hydroinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Hydroinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2166/hydro.2024.128\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydroinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2166/hydro.2024.128","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel application of waveform matching algorithm for improving monthly runoff forecasting using wavelet–ML models
The main goal of this study is to enhance the precision and reliability of monthly runoff forecasts within the complex Navrood watershed, situated in northern Iran. The innovative use of a waveform matching algorithm is a defining feature of this study. This approach is vital in optimizing the selection of the mother wavelet, which is a critical component in wavelet analysis. This is a significant divergence from established techniques in hydrological research, indicating a paradigm change in the area. To thoroughly assess model performance, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is applied. This all-encompassing evaluation guarantees not only astounding precision but also a near-perfect fit with the ideal solution. The findings highlight the remarkable precision attained by using the hybrid multiresolution analysis (MRA) methodology. The proposed methodology involves the integration of the maximal overlap discrete wavelet transform (MODWT) with a random forest (RF) model, referred to as MRA–RF. The obtained Nash–Sutcliffe efficiency (NSE) score of 0.94 is noteworthy. Furthermore, the model exhibits a low mean absolute error (MAE) of just 0.36 m3/s, a strong p-factor of 73.5%, and a significant d-factor of 37.9% during extensive testing.