Annual and non-monsoon rainfall prediction modelling using SVR-MLP: an empirical study from Odisha

Rainfall is a natural demolishing phenomenon. On the other side, it also serves as a major source of water when conserved through proper channel. For this issue, estimation of rain fall is of at utmost importance. The present study employed on rain fall forecasting in annual as well as non-moon session in Odisha (India). The total annual rainfall and relative humidity data were collected from period 1991-2015 from Department of Forest and Environment Govt. of Odisha. Support Vector Regression and Multilayer perception implemented for prediction of maximum rainfall in annual and non-monsoon session. Input parameter like average temperature in month, wind velocity, humidity, and cloud cover was conceder for predicting rainfall in non-monsoon session. The performance of the results was measure with MSE (mean squared error), correlation coefficient, coefficient of efficiency and MAE (mean absolute error). The results of SVR were compared to those of MLP and simple regression technique. MLP being a computationally intensive method, SVR could be used as an efficient alternative for runoff and sediment yield prediction under comparable accuracy in predictions.SVR-MLP may be used as promising alternative forecasting tool for higher accuracy in forecasting and better generalization ability

PREDICTION OF SOIL PORE WATER PRESSURE RESPONSES TO RAINFALL USING RADIAL BASIS KERNEL FUNCTION

Pore Water Pressure (PWP) prediction is important in analyzing the strength and effective stress of the soil. Increase of PWP will cause slope failure in areas susceptible to landslide. Stability is determined by the equalization of shear strength and shear stress analyses. Knowledge in pore water pressure is important in hydrological analysis, such as seepage slope strength analyses, engineered slope design and assessing slope reactions to rainfall. The main aim of this work is to forecast pore water pressure variations in response to rainfall utilizing Radial Basis Kernel Function and to evaluate model performance using statistical measures

Neural network emulation of a rainfall-runoff model

International audienceThe potential of an artificial neural network to perform simple non-linear hydrological transformations is examined. Four neural network models were developed to emulate different facets of a recognised non-linear hydrological transformation equation that possessed a small number of variables and contained no temporal component. The modeling process was based on a set of uniform random distributions. The cloning operation facilitated a direct comparison with the exact equation-based relationship. It also provided broader information about the power of a neural network to emulate existing equations and model non-linear relationships. Several comparisons with least squares multiple linear regression were performed. The first experiment involved a direct emulation of the Xinanjiang Rainfall-Runoff Model. The next two experiments were designed to assess the competencies of two neural solutions that were developed on a reduced number of inputs. This involved the omission and conflation of previous inputs. The final experiment used derived variables to model intrinsic but otherwise concealed internal relationships that are of hydrological interest. Two recent studies have suggested that neural solutions offer no worthwhile improvements in comparison to traditional weighted linear transfer functions for capturing the non-linear nature of hydrological relationships. Yet such fundamental properties are intrinsic aspects of catchment processes that cannot be excluded or ignored. The results from the four experiments that are reported in this paper are used to challenge the interpretations from these two earlier studies and thus further the debate with regards to the appropriateness of neural networks for hydrological modelling

An Exploration of Neural Network Modelling Options for the Upper River Ping, Thailand

This thesis reports results from a systematic experimental approach to evaluating aspects of the neural network modelling process to forecast river stage for a large, 23,600 km2 catchment in northern Thailand. The research is prompted by the absence of evidenced recommendations as to which of the array of input processes, validations and modelling procedures might be selected by a neural network forecaster. The flood issue for forecasters at Chiang Mai derives from the monsoon rainfall, which leads to serious out-of-bank flooding two to four times a year. Data for stage and rainfall is limited as the instrumentation is sparse and the historical flood record is limited in length. Neural network forecasting models are potentially very powerful forecasters where the data are limited. The challenge of this catchment is to provide adequate forecasts from data for relatively few storm events using three stage gauges and one rain gauge. Previous studies have reported forecasts with lead times of up to 18 hours. Thus, one research driver is to extend this lead time to give more warning. Eight input determination methods were systematically evaluated through thousands of model runs. The most successful method was found to be correlation and stepwise regression although the pattern was not consistent across all model runs. Cloud radar imagery was available for a few storm events. Rainfall data from a network was not available so it was decided to explore the value of the raw cloud reflectivity data as a catchment-wide surrogate for rainfall, to enhance the data record and potentially improve the forecast. The limited number of events makes drawing conclusions difficult, but for one event the forecast lead time was extended to 24-30 hours. The modelling also indicates that for this catchment where the monsoon may come from the south west or the north east, the direction of storm travel is important, indicating that developing two neural network models may be more appropriate. Internal model training and parameterisation tests suggest that future models should use Bayesian Regularization, and average across 50 runs. The number of hidden nodes should be less than the number input variables although for more complex problems, this was not necessarily the case. Ranges of normalisation made little difference. However, the minimum and maximum values used for normalisation appear to more important. The strength of the conclusions to be drawn from this research was recognised from the start as being limited by the data, but the results suggest that neural networks are both helpful modelling processes and can provide valuable forecasts in catchments with extreme rainfall and limited hydrological data. The systematic investigation of the alternative input determination methods, algorithms and internal parameters has enabled guidance to be given on appropriate model structures

River stage prediction based on a distributed support vector regression

Author name used in this publication: K. W. Chau2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Predicting next day’s production in run-of-river hydropower plants using machine learning

This master thesis focuses on time series forecasting using machine learning to predict hourly production in run-of-river hydropower plants. As electrical energy cannot be easily stored, it must be produced and consumed at the same time. To balance production and consumption, power suppliers in Norway must report to Statnett their expected energy production for the following day. Run-of-river hydropower plants rely on the natural water flow in rivers and do not involve any storage. Therefore, the production must be estimated based on the prevailing weather and hydrological conditions. Accurate predictions are crucial to minimize imbalance fees and ensure grid stability. The main objective of this study is to enhance the accuracy of predictions compared to Småkraft AS’s current method. The study aims to answer three research questions related to the most significant time intervals for weather variables that affect production, the most important features for making predictions, and the model’s performance in various weather situations. Two of Småkraft’s power plants, Bjørgum power plant and Furegardane power plant, provide the data for testing three machine learning models: a random forest regressor, a multilayer perception neural network, and a long short-term memory neural network. Input data to the models include weather forecast and observational-based variables, along with engineered features such as accumulated rainfall and snowmelt. The study concludes that the long short-term memory neural network is the best model and outperforms the current method used by Småkraft at both locations. The findings suggest that machine learning models can significantly improve the accuracy of hydropower production forecasting, which can benefit hydropower plant operators as well as the stability of the electricity grid. One limitation of the model is that it requires years of historical data and consequently will not be suitable for newly established power plants. Future work could focus on using data from power plants with comparable location, production patterns, and climate to predict the production in the power plant of interest.Masteroppgave i energiENERGI399I5MAMN-ENE