Evapotranspiration Modeling Using Second-Order Neural Networks

Adamala, Sirisha; Raghuwanshi, N. S.; Mishra, Ashok Kumar; Tiwari, Mukesh

doi:10.1061/(asce)he.1943-5584.0000887

Cited by 36 publications

(18 citation statements)

References 35 publications

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“…Training with higher number of hidden nodes might increase the performance of ANN models. But training with a several number of hidden nodes requires more computation time and cause complexity in architecture as it has to complete number of epochs [7]. Therefore, to avoid the above difficulty, the selection of an optimum node was fixed with a trial run of 1-15 hidden nodes only (i.e., not tried beyond 15 hidden nodes).…”

Section: Training Of Ann Models For Daily Et O Estimationmentioning

confidence: 99%

“…In contrast to conventional methods, ANNs can estimate ET o accurately with minimum climate data, which may have the advantages of being inexpensive, independent of specific climatic condition, ignored physical relations, and precise modeling of nonlinear complex system. In the last decade, many researchers have used ANN techniques for modeling of the ET o process [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

Adamala¹

2019

Advanced Evapotranspiration Methods and Applications

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Reference evapotranspiration (ET o) is an important and one of the most difficult components of the hydrologic cycle to quantify accurately. Estimation/measurement of ET o is not simple as there are number of climatic parameters that can affect the process. There exists copious conventional (direct and indirect) and non conventional/soft computing (artificial neural networks, ANNs) methods for estimating ET o. Direct methods have the limitations of measurement errors, expensive, impracticality of acquiring point measurements for spatially variable locations, whereas the indirect methods have the limitations of unavailability of all necessary climate data and lack of generalizability (needs local calibration). In contrast to conventional methods, soft computing models can estimate ET o accurately with minimum climate data which have advantages over limitations of conventional ET o methods. This chapter reviews the application of ANN methods in estimating ET o accurately for 15 locations in India using six climatic variables as input. The performance of ANN models were compared with the multiple linear regression (MLR) models in terms of root mean squared error, coefficient of determination and ratio of average output and target ET o values. The results suggested that the ANN models performed better as compared to MLR for all locations.

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Section: Training Of Ann Models For Daily Et O Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

Adamala¹

2019

Advanced Evapotranspiration Methods and Applications

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“…JST ini telah banyak digunakan dalam ilmu hidrologi untuk memrediksi arus sungai (Partal, 2009;Phukoetphim et al, 2014;Tiwari et al, 2012;Vafakhah, 2012), memrediksi curah hujan (Arif et al, 2012), evapotranspirasi (Adamala et al, 2014;Jahanbani, dan El-Shafie, 2011;Zanetti et al, 2007), banjir secara real time (Ghalkhani et al, 2013), dan banjir regional (Aziz et al, 2014) serta memrediksi kualitas air sungai (Asadollahfardi, et al, 2012;Huang dan Liu, 2010). Model yang dihasilkan oleh mereka rata-rata memberikan nilai efisiensi dan korelasi tinggi, misalnya yang dihasilkan oleh Tiwari et al (2012) memberikan nilai efisiensi 85%, atau Partal (2009) memberikan koefisien korelasi 0,90.…”

Section: Metode Penelitianunclassified

Prediction of water discharge and sediment in teak forested area using artificial neural network model

Wahyuningrum¹

2017

JPKW

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Informasi tentang hubungan curah hujan, debit aliran dan sedimen diperlukan dalam perencanaan pengelolaan DAS. Hubungan ini dikenal sangat nonlinear dan kompleks. Meskipun debit dan sedimen sudah dimonitor secara terus menerus, namun kadang kala informasinya tidak atau kurang lengkap. Dalam kondisi ini pemodelan sangat diperlukan. Tujuan penelitian adalah membuat model untuk memrediksi hasil air (direct runoff) dan sedimen bulanan dengan menggunakan Jaringan Syaraf Tiruan (JST). Model yang diuji menggunakan data hujan pada saat t-3 dan t-4 sebagai input, dan debit dan sedimen pada t+3 dan t+4 sebagai output. Data yang digunakan merupakan data tahun 2001 sampai dengan tahun 2014. Hasil penelitian menunjukkan bahwa dari beberapa model yang diuji terdapat 2 model untuk prediksi debit dan 2 model untuk prediksi sedimen. Model tersebut terpilih karena mempunyai MSE terkecil, R 2 terbesar dan K yang memuaskan (0,65). Dengan demikian, model-model tersebut dapat digunakan untuk memrediksi debit dan sedimen untuk jangka t+3 dan t+4. Prediksi debit t+3 dan t+4 dapat menggunakan persamaan berturut-turut Q t+3 = 0,64 Q t-3 + 0,05 dan Q t+4 = 0,65 Q t-4 + 0,074, sedangkan prediksi sedimen t+3 dan t+4 menggunakan persamaan QS t+3 = 0,45 QS t-3 + 0,052 dan QS t+4 = 0,45 QS t-4 + 0,052. Pemodelan dengan JST ini dapat diaplikasikan untuk memrediksi debit dan sedimen di lokasi yang lain dengan arsitektur yang disesuaikan dengan kondisi data yang tersedia. ABSTRACT Information on the relationship of rainfall with discharge and sediment are required in watershed management.This relationship is known to be highly nonlinear and complex. Although discharge and sediment has been monitored continuously, but sometimes the information is not or less complete. In this condition, modeling is indispensable.The research objective is to create a model to predict the monthly direct runoff and sediment using Artificial Neural Network (ANN).The model was tested using rainfall data at t-3 and t-4 as input, and discharge and sediment at t+3 and t+4 as output. The data used is the data from 2001 to 2014. The resultsshowed that of some models tested there are two models for the prediction of discharge and two models for sediment.The model was chosen because it has the smallest MSE, the largest R 2 and satisfying K (0.5 to 0.65

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“…The application of artificial neural networks (ANNs) for reference evapotranspiration (ET 0 ) modeling has received much attention in recent years (Trajkovic et al 2003;Trajkovic 2005;Kisi 2006aKisi , b, 2007aJain et al 2008;Kim and Kim 2008;Kumar et al 2009;Hamid et al 2011;Kilic 2011;Tabari et al 2012;Shirin Manesh et al 2013;Kim et al 2014;Adamala et al 2014;Deo and Sahin 2015). Trajkovic et al (2003) forecasted ET 0 using radial basis neural network (RBNN).…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al (2014) used two different ANN methods in estimation of ET 0 . Adamala et al (2014) utilized the second-order ANN method to model the ET 0 in different climatic zones of India. Gocic et al (2015) forecasted ET 0 data collected during the period 1980-2010 in Serbia using ANN and support vector machine.…”

Section: Introductionmentioning

confidence: 99%

An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration

Kisi

Kilic

2015

Theor Appl Climatol

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The generalization ability of artificial neural networks (ANNs) and M5 model tree (M5Tree) in modeling reference evapotranspiration (ET 0 ) is investigated in this study. Daily climatic data, average temperature, solar radiation, wind speed, and relative humidity from six different stations operated by California Irrigation Management Information System (CIMIS) located in two different regions of the USA were used in the applications. King-City Oasis Rd., Arroyo Seco, and Salinas North stations are located in San Joaquin region, and San Luis Obispo, Santa Monica, and Santa Barbara stations are located in the Southern region. In the first part of the study, the ANN and M5Tree models were used for estimating ET 0 of six stations and results were compared with the empirical methods. The ANN and M5Tree models were found to be better than the empirical models. In the second part of the study, the ANN and M5Tree models obtained from one station were tested using the data from the other two stations for each region. ANN models performed better than the CIMIS Penman, Hargreaves, Ritchie, and Turc models in two stations while the M5Tree models generally showed better accuracy than the corresponding empirical models in all stations. In the third part of the study, the ANN and M5Tree models were calibrated using three stations located in San Joaquin region and tested using the data from the other three stations located in the Southern region. Four-input ANN and M5Tree models performed better than the CIMIS Penman in only one station while the two-input ANN models were found to be better than the Hargreaves, Ritchie, and Turc models in two stations.

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Evapotranspiration Modeling Using Second-Order Neural Networks

Cited by 36 publications

References 35 publications

Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks

Prediction of water discharge and sediment in teak forested area using artificial neural network model

An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration

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