Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

Abstract

The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m ) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management. ARTICLE HISTORY

Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria

“…However, the models are developed using several combinations of the input variables to establish the best model while avoiding overfitting and underfitting problems 5,31,53 . For complex architectures, other methods, such as Gamma test, cross‐validation or bootstrapping approach can be used to overcome the earlier stated problems 30,54 . The input variables are preselected to be nine (denoted as X1, X2, X3 …, X9), where seven neurons are for the meteorological properties (rainfall, temperature, and others as given in Table 2) and the remaining two neurons are for the climate mode indices (SOI and NAO index).…”

“…5,31,53 For complex architectures, other methods, such as Gamma test, cross-validation or bootstrapping approach can be used to overcome the earlier stated problems. 30,54 The input variables are preselected to be nine (denoted as X1, X2, X3 … , X9), where seven neurons are for the meteorological properties (rainfall, temperature, and others as given in Table 2) and the remaining two neurons are for the climate mode indices (SOI and NAO index). The details of the input parameters are shown in Table 2.…”

“…5 Several statistical models, ranging from regression, Markov to the more recent machine learning approach have been used for forecasting drought and other climate-related parameters 5,[13][14][15][16][17][18][19][20][21] (Anurag et al, 2020). 22 Despite the achievements recorded by most of these methods; a simple, efficient, and well-established data-driven or statistical model is the artificial neural network (ANN). ANN works like a black box and it does not need detailed information about the input variable unlike the case of physical models.…”

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

“…In the last decades, machine learning-based models have proved to be a helpful alternative to deal with the multivariate and complex nature of the phenomena in various disciplines of engineering [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Optimized kernel logistic regression models were employed in [39] for landslide susceptibility assessment.…”

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

“…To overcome these difficulties, many researchers in recent years have tried to use indirect methods to predict Ep using meteorological parameters. Indirect methods include the use of artificial intelligence (AI) algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs), which have been widely used by researchers to extract the relationship between meteorological and Ep data (Bruton et al 2000;Ghorbani et al 2013;Kişi 2006;Malik et al 2020a;Seifi and Soroush 2020). The development of ANN and SVM intelligent models in a study aimed at estimating monthly Pan evaporation, conducted by (Eslamian et al 2008).…”