Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Abstract Finding an accurate computational method for predicting pan evaporation (EP), can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called Multiple Model-Support Vector Machine (MM-SVM) with the aim of increasing the accuracy of EP prediction on a monthly scale (EPm) in two meteorological stations (Ardabil and Khalkhal) using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)) were evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE) and coefficient of determination (R2)) and with the help of case visual indicators Were compared. According to the results of evaluation indicators in the test phase, two models MM-SVM-6 and ANN-5 with (RMSE, MAE, KGE and R2 equal to 1.088, 0.761, 0.79, 0.54 mm. month− 1, 0.819, 0.903 and 0.939, 0.962) and with three input variables, were introduced as the top models in Ardabil and Khalkhal stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs predicted by independent models, its power to predict EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in EPm prediction accuracy by this hybrid model under the above conditions (especially in Ardabil station) were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

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Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model

PeerJ ◽

10.7717/peerj.8043 ◽

2019 ◽

Vol 7 ◽

pp. e8043 ◽

Cited By ~ 3

Author(s):

Hafiza Mamona Nazir ◽

Ijaz Hussain ◽

Muhammad Faisal ◽

Elsayed Elsherbini Elashkar ◽

Alaa Mohamd Shoukry

Keyword(s):

Water Resources ◽

Water Resources Management ◽

Hybrid Model ◽

Series Data ◽

Support Vector ◽

Resources Management ◽

River Inflow ◽

Mode Decomposition ◽

Svm Model ◽

Non Linear

River inflow prediction plays an important role in water resources management and power-generating systems. But the noises and multi-scale nature of river inflow data adds an extra layer of complexity towards accurate predictive model. To overcome this issue, we proposed a hybrid model, Variational Mode Decomposition (VMD), based on a singular spectrum analysis (SSA) denoising technique. First, SSA his applied to denoise the river inflow data. Second, VMD, a signal processing technique, is employed to decompose the denoised river inflow data into multiple intrinsic mode functions (IMFs), each with a relative frequency scale. Third, Empirical Bayes Threshold (EBT) is applied on non-linear IMF to smooth out. Fourth, predicted models of denoised and decomposed IMFs are established by learning the feature values of the Support Vector Machine (SVM). Finally, the ensemble predicted results are formulated by adding the predicted IMFs. The proposed model is demonstrated using daily river inflow data from four river stations of the Indus River Basin (IRB) system, which is the largest water system in Pakistan. To fully illustrate the superiority of our proposed approach, the SSA-VMD-EBT-SVM hybrid model was compared with SSA-VMD-SVM, VMD-SVM, Empirical Mode Decomposition (EMD) based i.e., EMD-SVM, SSA-EMD-SVM, Ensemble EMD (EEMD) based i.e., EEMD-SVM and SSA-EEMD-SVM. We found that our proposed hybrid SSA-EBT-VMD-SVM model outperformed than others based on following performance measures: the Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Therefore, SSA-VMD-EBT-SVM model can be used for water resources management and power-generating systems using non-linear time series data.

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Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.35.23089 ◽

2018 ◽

Vol 7 (4.35) ◽

pp. 683

Author(s):

Nuratiah Zaini ◽

Marlinda Abdul Malek ◽

Marina Yusoff ◽

Siti Fatimah Che Osmi ◽

Nurul Hani Mardi ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Hybrid Model ◽

Peninsular Malaysia ◽

Coefficient Of Determination ◽

Support Vector ◽

Design Development ◽

Streamflow Forecasting ◽

Monthly Streamflow ◽

Bpnn Model

Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm – backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R2) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R2 for hybrid SVM-PSO are 24.8267 m3/s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m3/s and 0.9305 of R2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m3/s and R2, 0.7740 while BPNN model produces lower RMSE and R2 of 28.1396 m3/s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

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ECOLOGY AND SUSTAINABLE DEVELOPMENT IN LOCAL BUSINESS:SIMULATION OF LOW IMPACT DEVELOPMENT IN SURAKARTA REGENCY

eBA Journal: Journal Economics, Bussines and Accounting ◽

10.32492/eba.v4i1.542 ◽

2018 ◽

Vol 4 (1) ◽

pp. 32-38

Author(s):

Bhimo Rizky Samudro ◽

Yogi Pasca Pratama

Keyword(s):

Water Resources ◽

Water Resources Management ◽

River Flow ◽

Low Impact Development ◽

Development Model ◽

Business Activity ◽

Resources Management ◽

Central Java ◽

Specific Culture ◽

Central Java Province

This paper will describe the function of water resources to support business activities in Surakarta regency, Central Java province. Surakarta is a business city in Central Java province with small business enterprises and specific culture. This city has a famous river with the name is Bengawan Solo. Bengawan Solo is a River Flow Regional (RFR) to support business activities in Surakarta regency. Concious with the function, societies and local government in Surakarta must to manage the sustainability of River Flow Regional (RFR) Bengawan Solo. It is important to manage the sustainability of business activity in Surakarta regency. According to the condition in Surakarta regency, this paper will explain how the simulation of Low Impact Development Model in Surakarta regency. Low Impact Development is a model that can manage and evaluate sustainability of water resources in River Flow Regional (RFR). Low Impact Development can analys goals, structures, and process water resources management. The system can also evaluate results and impacts of water resources management. From this study, we hope that Low Impact Development can manage water resources in River Flow Regional (RFR) Bengawan Solo.

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