The utilization of a GR4J model and wavelet-based artificial neural network for rainfall–runoff modelling

Abstract Data-driven models and conceptual models have been utilized in an attempt to perform rainfall–runoff modelling. The aim of this study is comparing the performance of an artificial neural network (ANN) model, wavelet-based artificial neural network (WANN) model and GR4J lumped daily conceptual model for rainfall–runoff modelling of two rivers in the USA. It was obtained that the performance of the data-driven models (ANN, WANN) is better than the GR4J model especially when streamflow data the preceding day (Qt-1) and streamflow data the preceding two days (Qt-2) are used as input data in the ANN and WANN models for the simulation of low and high flows, in particular. On the other hand, when only precipitation and potential evapotranspiration data are used as input variables, the GR4J model performs better than the data-driven models.

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Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-015-1040-6 ◽

2015 ◽

Vol 29 (5) ◽

pp. 1345-1370 ◽

Cited By ~ 36

Author(s):

Guangyuan Kan ◽

Cheng Yao ◽

Qiaoling Li ◽

Zhijia Li ◽

Zhongbo Yu ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Driven ◽

Rainfall Runoff ◽

Runoff Simulation ◽

Hybrid Data ◽

Artificial Neural ◽

Event Based

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Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

International Journal of Chemical Reactor Engineering ◽

10.2202/1542-6580.1605 ◽

2007 ◽

Vol 5 (1) ◽

Author(s):

Yao Kouassi Benjamin ◽

Emmanuel Assidjo Nogbou ◽

Gossan Ado ◽

Catherine Azzaro-Pantel ◽

André Davin

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Operating Conditions ◽

Molar Ratio ◽

Ann Model ◽

Sequential Method ◽

Modeling And Optimization ◽

Artificial Neural ◽

Better Than

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ? X1 ? 0.77; 1.773 g ? X2 ? 1.86 g; 289.74 °C ? X3 ? 291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67% and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting.

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APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

Science and Technology Development Journal ◽

10.32508/stdj.v12i4.2237 ◽

2009 ◽

Vol 12 (4) ◽

pp. 94-106 ◽

Cited By ~ 1

Author(s):

Duc Van Le

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Research Trend ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Runoff Forecasting ◽

Artificial Neural ◽

Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

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Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

Civil Engineering Journal ◽

10.28991/cej-2017-00000074 ◽

2017 ◽

Vol 3 (2) ◽

pp. 78-87 ◽

Cited By ~ 7

Author(s):

Ajaykumar Bhagubhai Patel ◽

Geeta S. Joshi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

River Basin ◽

Annual Rainfall ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Feed Forward Back Propagation ◽

Sabarmati River ◽

Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

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An artificial neural network to estimate soil temperature

Canadian Journal of Soil Science ◽

10.4141/s96-062 ◽

1997 ◽

Vol 77 (3) ◽

pp. 421-429 ◽

Cited By ~ 16

Author(s):

Chun-Chieh Yang ◽

Shiv O. Prasher ◽

Guy R. Mehuys

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Soil Temperature ◽

Potential Evapotranspiration ◽

Daily Rainfall ◽

Coefficient Of Determination ◽

Ann Model ◽

Soil Temperatures ◽

Ann Models ◽

Artificial Neural

This study was undertaken to develop an artificial neural network (ANN) model for transient simulation of soil temperature at different depths in the profile. The capability of ANN models to simulate the variation of temperature in soils was investigated by considering readily available meteorologic parameters. The ANN model was constructed by using five years of meteorologic data, measured at a weather station at the Central Experimental Farm in Ottawa, Ontario, Canada. The model inputs consisted of daily rainfall, potential evapotranspiration, and the day of the year. The model outputs were daily soil temperatures at the depths of 100, 500 and 1500 mm. The estimated values were found to be close to the measured values, as shown by a root-mean-square error ranging from 0.59 to 1.82 °C, a standard deviation of errors from 0.61 to 1.81 °C, and a coefficient of determination from 0.937 to 0.987. Therefore, it is concluded that ANN models can be used to estimate soil temperature by considering routinely measured meteorologic parameters. In addition, the ANN model executes faster than a comparable conceptual simulation model by several orders of magnitude. Key words: Artificial neural networks, soil temperature, precipitation, potential evapotranspiration

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Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

Vietnam Journal of Computer Science ◽

10.1142/s2196888819500246 ◽

2019 ◽

Vol 06 (04) ◽

pp. 439-455 ◽

Cited By ~ 1

Author(s):

Nahian Ahmed ◽

Nazmul Alam Diptu ◽

M. Sakil Khan Shadhin ◽

M. Abrar Fahim Jaki ◽

M. Ferdous Hasan ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Satellite Image ◽

Population Estimation ◽

Data Driven ◽

Gradient Boosting ◽

Ann Model ◽

Data Driven Approach ◽

Artificial Neural

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

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Artificial Neural Network Model for Rainfall–Runoff Relationship

Jurnal Teknologi ◽

10.11113/jt.v37.524 ◽

2012 ◽

Cited By ~ 2

Author(s):

Sobri Harun ◽

Nor Irwan Ahmat Nor ◽

Amir Hashim Mohd. Kassim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Flood Control ◽

Mathematical Structure ◽

Data Sets ◽

Rainfall Runoff ◽

Ann Model ◽

Linear Relationships ◽

Artificial Neural ◽

Artificial Neural Network Ann

Permodelan bagi proses hidraulik dan hidrologi adalah penting apabila dilihat dari sudut kepelbagaian penggunaan sumber air seperti janakuasa hidroeletrik, pengairan, pengagihan bekalan air, dan kawalan banjir. Terdapat banyak kajian sebelum ini yang telah menggunakan kaedah rangkaian neural tiruan atau artificial neural network (ANN) untuk permodelan pelbagai perhubungan tak linear dan kompleks dalam proses hidrologi. Kaedah rangkaian neural tiruan ini telah diketahui bahawa ia merupakan suatu struktur matematik yang mudah ubah (flexible) dan berpotensi untuk menjana dan merumus set-set data masukan dan keluaran yang kurang tepat atau kabur dan tidak dihalusi dengan sempurna. Kawasan kajian adalah kawasan tadahan Sungai Lui (Selangor, Malaysia). Kertas Kerja ini mengutarakan cadangan menggunakan kaedah rangkaian neural tiruan ini bagi mendapatkan jumlah air larian permukaan harian dengan menggunakan hujan sebagai nod masukan kepada model berkenaan. Terdapat dua kaedah telah digunakan dalam pemilihan bilangan nod masukan iaitu seperti yang telah dicadangkan oleh [10] dan [5]. Seterusnya, hasil keputusan yang diperolehi daripada permodelan rangkaian neural tiruan ini dibandingkan dengan hasil keputusan yang diperolehi daripada model HEC-HMS. Didapati bahawa model rangkaian neural tiruan dapat menjana dan merumus perhubungan antara air larian permukaan dan curahan hujan lebih baik berbanding dengan model HEC-HMS. Kata kunci: hidrologi, rangkaian neural tiruan, hubungan air larian permukaan-curahan hujan The modelling of hydraulic and hydrological processes is important in view of the many uses of water resources such as hydropower generation, irrigation, water supply, and flood control. There are many previous works using the artificial neural network (ANN) method for modelling various complex non-linear relationships of hydrologic processes. The ANN is well known as a flexible mathematical structure and has the ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. The study area is Sungai Lui catchment (Selangor, Malaysia). This paper presents the proposed ANN model for prediction of daily runoff using the rainfall as input nodes. The method for selection of input nodes by [10] and [5] is applied. Further, the results are compared between ANN and HEC-HMS model. It has been found that the ANN models show a good generalization of rainfall-runoff relationship and is better than HEC-HMS model. Key words: hydrologic, artificial neural network, rainfall-runoff relationship

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Comparison between a Linear Regression and an Artificial Neural Network Model to Detect and Localize Damage in the Powder Mill Bridge

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120920631 ◽

2020 ◽

Vol 2674 (8) ◽

pp. 394-404

Author(s):

Kathryn Kaspar ◽

Erin Santini-Bell ◽

Marek Petrik ◽

Masoud Sanayei

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Linear Regression ◽

Structural Damage ◽

Data Driven ◽

Strain Gauges ◽

Average Error ◽

Ann Model ◽

Bridge Superstructure ◽

Artificial Neural

This paper evaluates the ability of two different data-driven models to detect and localize simulated structural damage in an in-service bridge for long-term structural health monitoring (SHM). Strain gauge data collected over 4 years is used to characterize the undamaged state of the bridge. The Powder Mill Bridge in Barre, Massachusetts, U.S., which has been instrumented with strain gauges since its opening in 2009, is used as a case study, and the strain gauges used in this study are located at 26 different stations throughout the bridge superstructure. A linear regression (LR) model and an artificial neural network (ANN) model are evaluated based on the following criteria: (a) the ability to accurately predict the strain at each location in the undamaged state of the bridge; (b) the ability to detect simulated structural damage to the bridge superstructure; and (c) the ability to localize simulated structural damage. Both the LR and the ANN models were able to predict the strain at the 26 stations with an average error of less than 5%, indicating that both methodologies were effective in characterizing the undamaged state of the bridge. A calibrated finite element model was then used to simulate damage to the Powder Mill Bridge for three damage scenarios: fascia girder corrosion, girder fracture, and deck delamination. The LR model proved to be just as effective as the ANN model at detecting and localizing damage. A recommended protocol is thus presented for integrating data-driven models into bridge asset management systems.

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Stem Taper Approximation by Artificial Neural Network and a Regression Set Models

Forests ◽

10.3390/f11010079 ◽

2020 ◽

Vol 11 (1) ◽

pp. 79 ◽

Cited By ~ 4

Author(s):

Jaroslaw Socha ◽

Pawel Netzel ◽

Dominika Cywicka

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Tree Species ◽

Stem Diameter ◽

Site Conditions ◽

Ann Model ◽

Stem Form ◽

Stem Taper ◽

Artificial Neural ◽

Better Than

Variation in tree stem form depends on species, age, site conditions, etc. Stem taper models that estimate stem diameter at any height and volume should comply with this complexity. In the paper, we propose new methods taking into account both unbiased estimates and stem variability: (i) an expert model based on an artificial neural network (ANN) and (ii) a statistical model built using a regression tree (REG). We used the variable-exponent taper equation (STE) as a reference for these two models. Input data contain information about 2856 trees representing eight dominant forest-forming tree species in Poland (birch, beech, oak, fir, larch, alder, pine, and spruce). The trees were selected across stands varied in terms of age and site conditions. Based on the data, we built ANN and REG models and calculated both stem taper and tree volumes. The results show that ANN is a universal approach that offers the most precise estimation of stem diameter at a particular stem height for different tree species. The results for alder are an exception. In this case, the REG model performs slightly better than ANN. In terms of volume prediction, the ANN model provides the most accurate predictions for coniferous and beech. In general, flexibility and predictive performance of the ANN are better than REG and reference the STE equation.

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