Modelling daily soil temperature at different depths via the classical and hybrid models
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This article is published in Meteorological Applications.The article was published on 2020-07-01 and is currently open access. It has received 20 citations till now. The article focuses on the topics: Adaptive neuro fuzzy inference system & Wavelet.read more
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Developing hybrid time series and artificial intelligence models for estimating air temperatures
Babak Mohammadi,Saeid Mehdizadeh,Farshad Ahmadi,Nguyen Thi Thuy Lien,Nguyen Thi Thuy Linh,Quoc Bao Pham +5 more
TL;DR: In this article, the authors proposed a hybrid time series model for estimating the air temperature at two weather stations located in Northwestern Iran for both daily and monthly time scales, and the results showed that the hybrid model performed better than the single AR for estimating air temperature parameters at the study sites.
Journal ArticleDOI
Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model
Meysam Alizamir,Sungwon Kim,Mohammad Zounemat-Kermani,Salim Heddam,Amin Hasanalipour Shahrabadi,Bahram Gharabaghi +5 more
TL;DR: The results indicate that the Deep ESN model outperformed conventional machine learning methods and can reduce the root mean square error (RMSE) accuracy of the traditional models between 30 and 60% in both stations.
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Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis
Journal ArticleDOI
Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow
Kegang Wang,Shahab S. Band,Rasoul Ameri,Meghdad Biyari,Tao Hai,Chung-Chain Hsu,Myriam Hadjouni,Hela Elmannai,Kwok Wing Chau,Amir Mosavi +9 more
TL;DR: In this paper , five different machine learning (ML) models were used to estimate monthly time-series river streamflow data at two hydrological stations in the USA (Heise and Irwin on Snake River, Idaho).
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Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India
TL;DR: The SMA algorithm-based SVM model had lower (higher) values of mean absolute error, root mean square error, and index of scattering and proved the better feasibility of SVM models in predicting daily ST at multiple depths on the study site.
References
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Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Decomposition of Hardy functions into square integrable wavelets of constant shape
A. Grossmann,J. Morlet +1 more
TL;DR: In this article, the authors studied square integrable coefficients of an irreducible representation of the non-unimodular $ax + b$-group and obtained explicit expressions in the case of a particular analyzing family that plays a role analogous to coherent states (Gabor wavelets) in the usual $L_2 $ -theory.
Wavelet Network Model and Its Application to the Prediction of Hydrology
Wensheng Wang,Jing Ding +1 more
TL;DR: Comparisons revealed that the suggested model could increase the forecasted accuracy and prolong the length time of prediction, and the wavelet network model is satisfied.
Journal ArticleDOI
On estimating soil surface temperature profiles
TL;DR: In this paper, the authors deal with two methods for modeling and estimating the daily and annual variation of soil surface temperature, which is an important factor for calculating the thermal performance of buildings in direct contact with the soil as well as for predicting the efficiency of earthto-air heat exchangers.
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