Evaporation estimation from climatic factors

Abstract

This study assessed the ability of two models, Local Linear Regression (LLR) and Artificial Neural Network (ANN) to estimate monthly potential evaporation from Pantagar, US Nagar (India) which falls under sub-humid and subtropical climatic zone. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation have been used to train and test the developed models. A comparison was made between the estimates provided by the LLR model and ANN model. Results shown that the models were able to well learn the events they were trained to recognize. For ANN model the correlation coefficient for training period is 0.9311 and for testing period is 0.9236 and the value of root mean square error for training period is 1.070 and for testing period it is 0.9863. In case of LLR model the correlation coefficient for training period is 0.9746 and for testing period is 0.9273 and value of root mean square error for training period is 0.6121 and for testing period it is 1.5301.

A Comparison between MLR, MARS, SVR and RF Techniques: Hydrological Time-series Modeling

“…In water resource modeling, Multivariate Adaptive Regression Splines (MARS) and Random Forest are successfully used [7]. Various learning algorithms have been used to create models by using meteorological variables to show their ability to perform pan evaporation [7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Forecasting of stage-discharge in a non-perennial river using machine learning with gamma test

“…Machine learning and artificial intelligence models have become very popular in recent decade [ [34] , [35] , [36] , [37] , [38] ]. Forecasting of the stream discharge various models such as multiple-linear regression (MLR) [ 2 , [39] , [40] , [41] , [42] ], rating curve [ [43] , [44] , [45] , [46] , [47] ], wavelet-based MLR (WMLR) [ 48 , 49 ], support vector machine (SVM) [ 39 , 44 , [50] , [51] , [52] , [53] ], artificial neural network (ANN) [ 45 , [53] , [54] , [55] , [56] , [57] ], wavelet-based artificial neural network (WANN) [ 2 , 39 , 58 ], adaptive neuro-fuzzy inference system (ANFIS) [ [59] , [60] , [61] ], wavelet-based support vector machine (WSVM) [ 39 , 62 ], wavelet–bootstrap–ANN (WBANN) [ 48 , 63 ], M5-model trees [ 46 , 64 ], random forest (RF) [ 65 ], ARIMA [ 65 , 66 ], gene expression programming (GEP) [ 32 , 67 , 68 ], genetic algorithm (GA) [ 3 , 33 , 69 ], genetic programming (GP) [ 32 ], Bagged M5P [ 65 ], integrating long-short-term memory (LSTM) [ 69 , 70 ], wavelet–bootstrap–multiple linear regression (WBMLR) [ 48 ], Fuzzy logic and fuzzy neuro systems [ 59 , 71 ] multi-objective evolutionary neural network (MOENN) [ 59 ], and Gaussian process regre...…”