Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants

Estimating future streamflows is a key step in producing electricity for countries with hydroelectric plants. Accurate predictions are particularly important due to environmental and economic impact they lead. In order to analyze the forecasting capability of models regarding monthly seasonal streamflow series, we realized an extensive investigation considering: six versions of unorganized machines—extreme learning machines (ELM) with and without regularization coefficient (RC), and echo state network (ESN) using the reservoirs from Jaeger’s and Ozturk et al., with and without RC. Additionally, we addressed the ELM as the combiner of a neural-based ensemble, an investigation not yet accomplished in such context. A comparative analysis was performed utilizing two linear approaches (autoregressive model (AR) and autoregressive and moving average model (ARMA)), four artificial neural networks (multilayer perceptron, radial basis function, Elman network, and Jordan network), and four ensembles. The tests were conducted at five hydroelectric plants, using horizons of 1, 3, 6, and 12 steps ahead. The results indicated that the unorganized machines and the ELM ensembles performed better than the linear models in all simulations. Moreover, the errors showed that the unorganized machines and the ELM-based ensembles reached the best general performances

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget

Global Ensemble Streamflow and Flood Modeling with Application of Large Data Analytics, Deep learning and GIS

ABSTRACTFlooding is one of the most dangerous natural disasters that repeatedly occur globally, and flooding frequently leads to major urban, financial, anthropogenic, and environmental impacts in the subjected area. Therefore, developing flood susceptibility maps to identify flood zones in the catchment is necessary for improved flood management and decision making. Streamflow and flood forecasting can provide important information for various applications including optimization of water resource allocations, water quality assessment, cost analysis, sustainable design of hydrological infrastructures, improvement in agriculture and irrigation practices. Compared to conventional or physically based hydrological modeling, which needs a large amount of historical data and parameters, the recent data-driven models which require limited amounts of data, have received growing attention among researchers due to their high predictive performance. This makes them more appropriate for hydrological forecasting in basin-scale and data-scarce regions. In this context, the main objective of this study was to evaluate the performance of various data-driven modeling approaches in flood and streamflow forecasting. One of the significant desires in daily streamflow prediction in today’s world is recognizing possible indicators and improving their applicability for effective water management strategies. In this context, the authors proposed an ensemble data mining algorithm coupled with various machine learning methods to perform data cleaning, dimensionality reduction, and feature subset selection. To perform the task of data mining, three data cleaning approaches: Principle Component Analysis (PCA), Tensor Flow (TF) and Tensor Flow K-means clustering(TF-k-means clustering) have been used. For the feature selection, four different machine learning approaches including K Nearest Neighbor (KNN), Bootstrap aggregating, Random Forest (RF) and Support Vector Machin (SVM) have been investigated. Out of twelve different combinations of data mining and machine learning, the best ensemble model was TF-k-means clustering coupled with RF, which outperformed the other methods with 96.52% classification accuracy. Thereafter, a modified Nonlinear Echo State Networks Multivariate Polynomial (NESN-MP) named in the current study as Robust Nonlinear Echo State Network (RNESN) was utilized for daily streamflow forecasting. The RNESN decreases the size of the reservoir (hidden layer which performs random weigh initialization), reduces the computational burden compared with NESN-MP, and increases the interactions between the internal states. The model is thus simple and user-friendly with better learning ability and more accurate forecasting performance. The method has been tested with data provided by the United States Geological Survey (USGS), Natural Resource Conservation Service (NRCS), National Weather Service Climate Prediction Center (NOAA) and Daymet Data Set from NASA through the Earth Science Data and Information System (ESDIS). Each data set includes the daily records of the local observed hydrological and large-scale weather/climate variability parameters. The efficiency of the proposed method has been evaluated in three regions namely Berkshire County (MA), Tuolumne County (CA), and Wasco County (OR). These basins were designated based upon the wide range of climatic conditions across the US that they represent. The simulation results were compared with NESN-MP and Adaptive Neuro-Fuzzy Inference System (ANFIS). The results validate the superiority of the proposed modeling approach compared to NESN-MP and ANFIS. The proposed RNESN approaches outperform the other methods with an RMSE = 0.98. For flood forecasting, an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, has been proposed to prepare the flood susceptibility map. In in this study, we proposed a new ensemble of models of Bootstrap aggregating as a Meta classifier based upon the K-Nearest Neighbor (KNN) functions including coarse, cosine, cubic and weighted as base classifiers to perform spatial prediction of the flood. We first selected 10 conditioning factors to spatial prediction of floods and then their prediction capability using the relief-F attribute evaluation (RFAE) method was assessed. Model validation was performed using two statistical error-indexes and the area under the curve (AUC). Results concluded that the Bootstrap aggregating -cubic KNN ensemble model outperformed the other ensemble models. Therefore, the Bootstrap aggregating -cubic KNN model can be used as a promising technique for the sustainable management of flood-prone areas. Furthermore, the AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%. The results showed that the EBF model had the highest accuracy in predicting the flood susceptibility map, in which 14% of the total areas were located in high and very high susceptibility classes and 62% were located in low and very low susceptibility classes

Forecasting Particulate Matter Concentrations: Use of Unorganized Machines

Air pollution is an environmental issue studied worldwide, as it has serious impacts on human health. Therefore, forecasting its concentration is of great importance. Then, this study presents an analysis comprising the appliance of Unorganized Machines – Extreme Learning Machines (ELM) and Echo State Networks (ESN) aiming to predict particulate matter with aerodynamic diameter less than 2.5 m (PM2.5) and less than 10 m (PM10). The databases were from Kallio and Vallilla stations in Helsinki, Finland. The computational results showed that the ELM presented best results to PM2.5, while the ESN achieved the best performance to PM10

An evaluation of CNN and ANN in prediction weather forecasting: A review

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways.  People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice

Hypertuned temporal fusion transformer for multi-horizon time series forecasting of dam level in hydroelectric power plants

This paper addresses the challenge of predicting dam level rise in hydroelectric power plants during floods and proposes a solution using an automatic hyperparameters tuning temporal fusion transformer (AutoTFT) model. Hydroelectric power plants play a critical role in long-term energy planning, and accurate prediction of dam level rise is crucial for maintaining operational safety and optimizing energy generation. The AutoTFT model is applied to analyze time series data representing the water storage capacity of a hydroelectric power plant, providing valuable insights for decision-making in emergency situations. The results demonstrate that the AutoTFT model surpasses other deep learning approaches, achieving high accuracy in predicting dam level rise across different prediction horizons. Having a root mean square error (RMSE) of 2.78×10−3 for short-term forecasting and 1.72 considering median-term forecasting, the AutoTFT shows to be promising for time series prediction presented in this paper. The AutoTFT had lower RMSE than the adaptive neuro-fuzzy inference system, long short-term memory, bootstrap aggregation (bagged), sequential learning (boosted), and stacked generalization ensemble learning approaches. The findings underscore the potential of the AutoTFT model for improving operational efficiency, ensuring safety, and optimizing energy generation in hydroelectric power plants during flood events

Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America

The emission of pollutants from vehicles is presented as a prime factor deteriorating air quality. Thus, seeking public policies encouraging the use and the development of more sustainable vehicles is paramount to preserve populations&rsquo health. To better understand the health risks caused by air pollution and exclusively by mobile sources urges the question of which input variables should be considered. Therefore, this research aims to estimate the impacts on populations&rsquo health related to road transport variables for S&atilde o Paulo, Brazil, the largest metropolis in South America. We used three Artificial Neural Networks (ANN) (Multilayer Perceptron&mdash MLP, Extreme Learning Machines&mdash ELM, and Echo State Neural Networks&mdash ESN) to estimate the impacts of carbon monoxide, nitrogen oxides, ozone, sulfur dioxide, and particulate matter on outcomes for respiratory diseases (morbidity&mdash hospital admissions and mortality). We also used unusual inputs, such as road vehicles fleet, distributed and sold fuels amount, and vehicle average mileage. We also used deseasonalization and the Variable Selection Methods (VSM) (Mutual Information Filter and Wrapper). The results showed that the VSM excluded some variables, but the best performances were reached considering all of them. The ELM achieved the best overall results to morbidity, and the ESN to mortality, both using deseasonalization. Our study makes an important contribution to the following United Nations Sustainable Development Goals: 3&mdash good health and well-being, 7&mdash affordable and clean energy, and 11&mdash sustainable cities and communities. These research findings will guide government about future legislations, public policies aiming to warranty and improve the health system. Document type: Articl

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply

Solar Irradiance Forecasting Using Dynamic Ensemble Selection

Solar irradiance forecasting has been an essential topic in renewable energy generation. Forecasting is an important task because it can improve the planning and operation of photovoltaic systems, resulting in economic advantages. Traditionally, single models are employed in this task. However, issues regarding the selection of an inappropriate model, misspecification, or the presence of random fluctuations in the solar irradiance series can result in this approach underperforming. This paper proposes a heterogeneous ensemble dynamic selection model, named HetDS, to forecast solar irradiance. For each unseen test pattern, HetDS chooses the most suitable forecasting model based on a pool of seven well-known literature methods: ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The experimental evaluation was performed with four data sets of hourly solar irradiance measurements in Brazil. The proposed model attained an overall accuracy that is superior to the single models in terms of five well-known error metrics