Simulation of suspended sediment based on gamma test,heuristic, and regression-based techniques

In the present study, four different heuristic techniques viz. multi-layer perceptron (MLP), radial basis function (RBF), self-organizing maps (SOM), and co-active neuro-fuzzy inference system (CANFIS) with hyperbolic tangent and sigmoid transfer functions and two regression-based techniques, i.e., multiple linear regression (MLR) and sediment-rating curve (SRC), were used for suspended sediment modeling. Gamma test (GT), correlation function (CF), M test, and trail–error procedure were applied for estimation of appropriate input variables as well as training data length. The results of the GT and CF suggested the five input variables (Q_t, Q_t?1, Q_t?2, S_t?1, and S_t?2, where Q_t?1 and S_t?1 indicate the discharge and sediment values of one previous day) as the best combination. The optimal training data length (75% of total data) was estimated by M test and trail–error procedure for development of the applied models. The MLP with sigmoid transfer function (M-2) performed better than the all other models. The results of sensitivity analysis indicated that the present-day discharge (Q_t), 1-day lag discharge (Q_t?1) and 1-day lag suspended sediment (S_t?1) are the most influenced parameters in modeling current day suspended sediment (S_t).     

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

ABSTRACT

Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to the knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM) are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have a substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow.     

Deep echo state network: a novel machine learning approach to model dew point temperature using meteorological variables

ABSTRACT

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (T_dew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting T_dew values.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

Predicting daily pan evaporation by soft computing models with limited climatic data

Abstract

Accurate prediction of daily pan evaporation (PE) is important for monitoring, surveying, and management of water resources as well as reservoir management and evaluation of drinking water supply systems. This study develops and applies soft computing models to predict daily PE in a dry climate region of south-western Iran. Three soft computing models, namely the multilayer perceptron-neural networks model (MLP-NNM), Kohonen self-organizing feature maps-neural networks model (KSOFM-NNM), and gene expression programming (GEP), were considered. Daily PE was predicted at two stations using temperature-based, radiation-based, and sunshine duration-based input combinations. The results obtained by the temperature-based 3 (TEM3) model produced the best results for both stations. The Mann-Whitney U test was employed to compute the rank of different input combination for hypothesis testing. Comparison between the soft computing models and multiple linear regression model (MLRM) demonstrated the superiority of MLP-NNM, KSOFM-NNM, and GEP over MLRM. It was concluded that the soft computing models can be successfully employed for predicting daily PE in south western Iran.

Editor D. Koutsoyiannis     

River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

ABSTRACT

Suspended sediment load (SSL) is one of the essential hydrological processes that affects river engineering sustainability. Sediment has a major influence on the operation of dams and reservoir capacity. This investigation is aimed at exploring a new version of machine learning models (i.e. data mining), including M5P, attribute selected classifier (AS M5P), M5Rule (M5R), and K Star (KS) models for SSL prediction at the Trenton meteorological station on the Delaware River, USA. Different input scenarios were examined based on the river flow discharge and sediment load database. The performance of the applied data mining models was evaluated using various statistical metrics and graphical presentation. Among the applied data mining models, the M5P model gave a superior prediction result. The current and one-day lead time river flow and sediment load were the influential predictors for one-day-ahead SSL prediction. Overall, the applied data mining models achieved excellent predictions of the SSL process.     

Trend analysis of rainfall pattern over the Central India during 1901–2010

In the present study, trends of rainfall of the Central India were evaluated in monthly, seasonal, and annual time scales using the Revised Mann-Kendall (RMK) test, Sen’s slope estimator, and innovative trend method (ITM). For this purpose, the monthly rainfall data for 20 stations in Madhya Pradesh (MP) and Chhattisgarh (CG) states in Central India during 1901–2010 was used. The Sen’s slope estimator was utilized for calculating the slope of rainfall trend line. Based on the obtained results of RMK test, there is no significant trend in the stations for the January and October months. The results also showed that for MP, two out of 15 considered stations indicate significant annual trend, while the CG has four out of five stations with significant trend. The results of applying ITM test indicated that most of the stations have decreasing trends in annual (16 stations), summer (16 stations), and monsoon (11 stations) seasons, while the winter (12 stations) and post monsoon (11 stations) seasons generally show increasing trend. Unlike the RMK, the ITM shows significant increasing trend in rainfall of November and December months. The finding of current study can be used for irrigation and water resource management purpose over the Central India.     

Evapotranspiration modelling from climatic data using a neural computing technique

Evapotranspiration is one of the basic components of the hydrologic cycle and essential for estimating irrigation water requirements. This paper investigates the modelling of evapotranspiration using the feed‐forward artificial neural network (ANN) technique with the Levenberg–Marquardt (LM) training algorithm. The LM algorithm has never been used in evapotranspiration estimation before. The LM is used for the optimization of network weights, since this algorithm is more powerful and faster than the conventional gradient descent. Various combinations of daily climatic data, i.e. wind speed, air temperature, relative humidity and solar radiation, from three stations in Los Angeles, USA, are used as inputs to the ANN so as to evaluate the degree of effect of each of these variables on evapotranspiration. A comparison is made between the estimates provided by the ANN and those of the following empirical models: Penman, Hargreaves, Turc. Mean square error, mean absolute error and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it was found that the neural computing technique could be employed successfully in modelling evapotranspiration process from the available climatic data. The results also indicate that the Hargreaves method provides better performance than the Penman and Turc methods in estimation of the evapotranspiration. The accuracy of the ANN technique in evapotranspiration estimation using nearby station data was also investigated. Copyright © 2007 John Wiley & Sons, Ltd.     

Modeling River Stage‐Discharge Relationships Using Different Neural Network Computing Techniques

One of the most important problems in hydrology is the establishment of rating curves. The statistical tools that are commonly used for river stage‐discharge relationships are regression and curve fitting. However, these techniques are not adequate in view of the complexity of the problems involved. Three different neural network techniques, i. e., multi‐layer perceptron neural network with Levenberg‐Marquardt and quasi‐Newton algorithms and radial basis neural networks, are used for the development of river stage‐discharge relationships by constructing nonlinear relationships between stage and discharge. Daily stage and flow data from three stations, Yamula, Tuzkoy and Sogutluhan, on the Kizilirmak River in Turkey were used. Regression techniques are also applied to the same data. Different input combinations including the previous stages and discharges are used. The models' results are compared using three criteria, i. e., root mean square errors, mean absolute error and the determination coefficient. The results of the comparison reveal that the neural network techniques are much more suitable for setting up stage‐discharge relationships than the regression techniques. Among the neural network methods, the radial basis neural network is found to be slightly better than the others.