scholarly journals Monthly Rainfall-Runoff Modeling for Sondur Reservoir

2021 ◽  
Vol 23 (07) ◽  
pp. 1453-1459
Author(s):  
Shashi Kant Jaiswal ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Development ◽  
Monthly Rainfall ◽  
Nonlinear Mapping ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Regression Procedure ◽  
Runoff Modeling ◽  
Artificial Neural Network Ann

This study presents the application of Artificial Neural Network (ANN) to modeling the rainfall-inflow relationship for Sondur Reservoir located in Chhattisgarh State of India. ANNs are usually assumed to be powerful tools for nonlinear mapping in various applications. ANN is superior to linear regression procedure used for rainfallinflow modeling. For model development twenty nine years data of monthly rainfall and inflow have been used. The results extracted from study indicated that the ANN model is efficient for rainfall-inflow modeling.

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

2009 ◽  
Vol 12 (4) ◽  
pp. 94-106 ◽  
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.

scholarly journals ÁRVORE MODELO FRENTE A UMA REDE NEURAL ARTIFICIAL PARA A MODELAGEM CHUVA-VAZÃO

Nativa ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 527
Author(s):  
Aline Bernarda Debastiani ◽  
Sílvio Luís Rafaeli Neto ◽  
Ricardo Dalagnol da Silva
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Tree ◽  
Superior Performance ◽  
Multi Layer Perceptron ◽  
Rainfall Runoff ◽  
Model Tree ◽  
Runoff Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

O objetivo deste estudo é investigar o desempenho da árvore modelo (M5P) e sua sensibilidade à poda e comparação com o desempenho de uma Rede Neural Artificial (RNA) para a simulação da vazão média diária mensal. A motivação para esta análise está na maior simplicidade e velocidade de processamento da M5P comparado às RNAs e a carência de estudos aplicando este método na modelagem hidrológica. O estudo foi desenvolvido na bacia hidrográfica do Alto Canoas, tendo um delineamento experimental composto por um período de treinamento, um de validação cruzada e dois períodos de testes. A RNA utilizada foi a Multi Layer Perceptron (MLP), implementada no software MATLAB, e a M5P (com e sem poda), disponível do software WEKA. O algoritmo M5P se mostrou sensível à poda em somente metade dos tratamentos. A M5P apresentou bom ajuste na modelagem, porém a RNA apresentou desempenho superior em todos os tratamentos.Palavras-chave: rede neural artificial; árvore de regressão; Bacia do Alto Canoas. MODEL TREE IN COMPARISON TO ARTIFICIAL NEURAL NETWORK FOR RAINFALL-RUNOFF MODELING ABSTRACT: The aim of this study is to investigate the performance of the model tree (M5P) and its sensitivity to pruning and comparison to the performance of an Artificial Neural network (ANN) for the simulation of daily average discharge of the month. The motivation for this analysis is on simplicity and speed of processing M5P compared the RNAs. The study was developed in the Alto Canoas watershed, having an experiment consisting of a training period, a cross-validation and two testing periods. The ANN used was the Multi Layer Perceptron (MLP), implemented in MATLAB software, and M5P (with and without pruning), available from the WEKA software. M5P algorithm proved sensitive to pruning in half of the treatments. The M5P showed good fit in the modeling, but the RNA presented superior performance in all treatments.Keywords: artificial neural network; regression tree; Basin Alto Canoas.

Artificial Neural Network Model for Rainfall–Runoff Relationship

2012 ◽  
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

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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