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In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value. [ABSTRACT FROM AUTHOR]

Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework. [ABSTRACT FROM AUTHOR]

Wildfires have caused devastating consequences to property and human and animal lives, which has become a global problem. Consequently, advanced wildfire prediction models are required to treat complex features and climate conditions. As a result, Machine Learning and Deep Learning models are becoming popular. However, creating a balanced true and false labeled dataset in the wild-fire domain is often challenging. Hence, One-class classification models are a promising approach to overcome this concern. In this paper, several One-class classification models are investigated; linear models (Principal Component Analysis and One-Class Support Vector Machines), outlier ensemble models (Lightweight On-line Detector of Anomalies and Locally Selective Combination of Parallel Outlier Ensembles), proximity-based models (Histogram-based Outlier Score and Rotation-based Outlier Detection), probabilistic models (Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions and Copula-Based Outlier Detection), neural network-based models (Deep One-class Classification and Adversarially Learned Anomaly Detection) is used in two case studies for California and Western Australian states. In conclusion, it was found that Deep learning-based One-class classification models outperform other models in terms of performance and feature importance of showcasing the effectiveness of deep neural network models in the wildfire prediction domain. [ABSTRACT FROM AUTHOR]

The current study aims at predicting the strength of the problematic clayey soils treated with combinations of pozzolan of natural sources and lime powder when added as soil additives at a nano scale. Multiple linear regression (MLR), artificial neural networks (ANN) and fuzzy logic (FL) tools were employed in the analytical study. The variables of the present study include the following: nano pozzoaln of natural source (NNP) content, nano lime content (NL), median particle size of NNP, active silica content of NNP (SiO2active), Initial liquid limit (ILL) and initial plastic limit (IPL) of the investigated soils. NNP was added at five percentages, i.e. 0%, 0.5%, 1%, 1.5% and 2%, while NL was added at five percentages, i.e. 0%, 0.3%, 0.6%, 0.9% and 1.2%. Three median particle sizes namely 50, 100 and 500 nm size were studied. Based on the different investigated soils and combinations, 120 soil mixtures were prepared and tested. California bearing ratio (CBR) and plasticity index (PI) were particularly examined. CBR tests were conducted at a soaked condition on specimens compacted to a maximum dry density (MDD) at the optimum moisture content (OMC). PI values were obtained following the Atterberg limits test. Based on the results of the performance criteria of the developed predictive models, it can be concluded that the CBR and PI of the expansive clayey soils can be effectively predicted using ANN and FL techniques. The results obtained by MLR were far from those obtained by both ANN & FL. In addition, ANN tool was slightly more accurate than FL as far as prediction of CBR and PI is concerned. The higher capability of ANN & FL models in predicting CBR & PI values, which generally obtained through time-consuming and expensive tests, could be useful for geotechnical engineers to assess or design a new pavement project. Further, it is recommended to do a re-evaluation of the current study in future, particularly when more data is available in the literature. [ABSTRACT FROM AUTHOR]

The objective of the research is to estimate the value of the California bearing ratio (CBR) through the application of ANN. The methodology consists of creating a database with soil index and CBR variables of the subgrades and granular base of pavements in Jaen, Peru, carried out in the soil mechanics laboratories of the city and the National University of Jaen. In addition, the Python library Seaborn is for variable selection and relevance, and the scikit-learn and Keras libraries were used for the learning, training, and validation stage. Five ANN are proposed to estimate the CBR value, obtaining an error of 4.47% in the validation stage. It can be concluded that this method is effective and valid to determine the CBR value in subgrades and granular bases of any pavement for its evaluation or design. [ABSTRACT FROM AUTHOR]

Energy use forecasting is crucial in balancing the electricity supply and demand to reduce the uncertainty inherent in the inter-basin water transfer project. Energy use prediction supports the reliable water-energy supply and encourages cost-effective operation by improving generation scheduling. The objectives are to develop subsequent monthly energy use predictive models for the Mokelumne River Aqueduct in California, US. Partial objectives are to (a) compare the model performance of a baseline model (multiple linear regression (MLR)) to three machine learning-based models (random forest (RF), deep neural network (DNN), support vector regression (SVR)), (b) compare the model performance of the whole system to three subsystems (conveyance, treatment, distribution), and (c) conduct sensitivity analysis. We simulate a total of 64 cases (4 algorithms (MLR, RF, DNN, SVR) x 4 systems (whole, conveyance, treatment, distribution) x 4 scenarios (different combinations of independent variables). We concluded that the three machine learning algorithms showed better model performance than the baseline model as they reflected non-linear energy use characteristics for water transfer systems. Among the three machine learning algorithms, DNN models yielded higher model performance than RF and SVR models. Subsystems performed better than the whole system as the models more closely reflected the unique energy use characteristics of the subsystems. The best case was having water supply (t), water supply (t-1), precipitation (t), temperature (t), and population (y) as independent variables. These models can help water and energy utility managers to understand energy performance better and enhance the energy efficiency of their water transfer systems. [ABSTRACT FROM AUTHOR]

Sedimentation is an important aspect of water resources management with many implications. Often, process‐based methods are employed to predict and assess the amount of sediment in water, but there are still challenges because the mechanisms that govern sediment transport are not yet fully understood. Furthermore, complex domains make model calibration difficult. Thus, as a complementary tool, a machine‐learning model was developed in the present study to emulate an existing process‐based model in simulating suspended sediment concentration (SSC). It employs the long short‐term memory (LSTM) networks, which are a type of artificial neural networks (ANNs) designed for supervised learning of a sequence of data (e.g., time series). The model was applied to the Sacramento‐San Joaquin Delta (the Delta) of California, USA, which is characterized by an interconnected system of sloughs, waterways, and a tidal outlet. The model training was performed with historical records of flow, stage and SSC at various locations within the Delta. The study period was 2010 through 2016, but the training period (i.e., range of observed data used to train the model) was varied to assess the model's sensitivity to the inputs and to determine the optimum model setup. Comparison between the model‐estimated SSC and the observation at 12 key locations within the Delta showed that the estimation accuracy of the LSTM model during the study period is comparable or superior to that of the Delta Simulation Model II‐General Transport Model (DSM2‐GTM), a process‐based operational hydrodynamics and water quality model for the Delta. In terms of the ratio of the root‐mean‐square error to the standard deviation (RSR), LSTM models generally showed higher predictability than DSM2‐GTM in all test cases investigated, with the lowest (most desirable) and highest (least desirable) LSTM‐based RSR being 0.21 and 1.14, respectively. In comparison, the lowest and highest RSR values with DSM2‐GTM were 0.26 and 3.70, respectively. The median LSTM‐based RSR of all study locations is around 0.7 while its DSM2‐GTM counterpart is about 1.0. LSTM models also yielded remarkably higher (more desirable) Nash‐Sutcliffe Efficiency values. Moreover, visual inspection found that LSTM models better captured the timing and magnitude of peaks as well as the temporal variations in the SSC time series. The LSTM model's performance was further analysed with hydro‐meteorological data (precipitation and wind speed) incorporated in training. While precipitation led to some improvement, the wind speed was found to have a negligible effect overall. All in all, the study findings suggest that the LSTM model has the potential to supplement the operational process‐based model in guiding water resources planning and management practices. [ABSTRACT FROM AUTHOR]

The levels of total salinity and its ion constituents in estuarine environments are important indicators of the overall suitability of water for environmental, agricultural, and urban use. These constituents include Total Dissolved Solids (TDS), dissolved chloride (Cl−), dissolved sulfate (SO42−), dissolved sodium (Na+), dissolved calcium (Ca2+), dissolved magnesium (Mg2+), dissolved nitrate (NO3), dissolved potassium (K), dissolved bromide (Br−), dissolved boron (B), Alkalinity, and hardness, among others. In practice, salinity is typically measured indirectly as electrical conductance (EC) via automatic sensors while the concentration of each constituent is often measured from discrete water samples (i.e., grab samples) and thus is available much less frequently than salinity. Quadratic regression equations are generally developed between salinity (as the predictor, represented by EC) and individual constituents (as the predictand) based on grab sample data. The regression models are then applied to estimate the concentrations of individual constituents given EC during the period when grab samples are not available. The current study develops four types of machine learning models: the Generalized Additive Model, Regression Trees, Random Forest, and Artificial Neural Networks, to emulate conventional regression equations in salinity-constituent conversion. A case study in the South Sacramento-San Joaquin Delta of California is presented to illustrate the development and application of these models and to compare their performance with that of the benchmark regression models. The results indicate that machine learning models can provide comparable or superior simulations to the regression models. Among the four machine models examined, the Random Forest model tends to yield the best results. The study further discusses the scientific and practical implications of the models proposed, their limitations, as well as future work to further improve their performance and applicability. [ABSTRACT FROM AUTHOR]

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 and 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1–6 h of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available. [ABSTRACT FROM AUTHOR]

In seismic structural health monitoring (SHM), a structure is normally instrumented with limited sensors at certain locations to monitor its structural behavior during an earthquake event. To reconstruct the responses at non-instrumented locations, an effective regression method has to be used given the measured data from the sensed locations. In addition, determination of where to place the sensors directly affects the ability of the system to infer the behaviour of the entire structure. In this study, a practical framework is proposed for sensor placement and seismic response reconstruction at non-instrumented locations, which adopts a novel attention-based deep neural network (DNN). The developed DNN model is trained by using structural displacements at measured locations as input and the structural displacements at unmeasured locations of interest as output. The proposed framework is demonstrated by a case study of an instrumented long-span girder bridge in California. Different sensor placement schemes are investigated using the proposed DNN model. Real-time seismic assessment of the bridge is achieved by issuing each reconstructed output in 1.5 ms. The case study validates the effectiveness and accuracy of the proposed method to be used as part of a seismic SHM system. [ABSTRACT FROM AUTHOR]

Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used process model, the Delta Simulation Model II (DSM2), used for salinity estimation in California's Sacramento-San Joaquin Delta (Delta). We apply historical daily input data to DSM2 and corresponding salinity simulations at 28 study locations from 1990 to 2019 to train two machine-learning models: a multi-layer perceptron (MLP) and Long-Short-Term Memory (LSTM) networks in a multi-task learning framework. We assess sensitivity of both networks to the amount of antecedent input information (memory) and training data to determine appropriate memory size and training data length. We evaluate network performance according to several statistical metrics as well as visual inspection. The study further investigates two additional networks, the Gated Recurrent Unit (GRU) and Residual Network (ResNet) in salinity modeling, and compares their efficacy against MLP and LSTM. Our results demonstrate strong performance of the four neural network models over the study period, achieving absolute bias below 4%, plus near-perfect correlation coefficients and Nash–Sutcliffe efficiency coefficients. The high complexity LSTM shows slight performance edge. We further show that deeper and wider versions of MLP and LSTM yield only marginal benefit over their baseline counterparts. We also examined issues related to potential overfitting by the proposed models, training data selection strategies, and analytical and practical implications. Overall, this new study indicates that machine-learning-based emulators can efficiently emulate DSM2 in salinity simulation. They exhibit strong potential to supplement DSM2 in salinity modeling and help guide water resource planning and management practices for the Delta region. [ABSTRACT FROM AUTHOR]

• A prediction methodology to predict wind and solar power curtailments in CAISO. • Machine learning-based forecasting using cross-validation and hold-out approaches. • Analyzing the six-year hourly data of power plants, power exchange, and demand. • A correlation matrix of eight input features and the three target variables. • Cross-validation improved the prediction of oversupply using random forest method. The economic viability of renewable energy is deteriorating due to its curtailment in power systems. Therefore, it is imperative to forecast curtailments for more effective utilization. To alleviate this issue, in this paper, we propose artificial intelligent-based models to accurately predict wind and solar power curtailments (WSPCs), which have not been investigated before. In this regard, a prediction methodology is developed using different types of machine learning (ML) methods and evaluated based on both hold-out (HO) and cross-validation (CV) approaches. The ML methods considered include regression trees (RT), gradient boosting trees (GBT), random forest (RF), feed-forward artificial neural networks (ANN), long short-term memory (LSTM), and support vector machines (SVR). The prediction models are trained based on eight input features, including load demands, the output power of thermal power plants, nuclear units, solar farms, wind turbines, biomass/geothermal units, large hydro units, power imports, and WSPC as two target variables. Based on historical data, i.e., hourly records of California independent system operator (ISO), the predictive models are validated, and the optimal hyperparameters are chosen using Bayesian optimization for each model to attain the best results. Among all the models, the RF model results in the minimum prediction errors and thus the best performance by implementing the proposed CV approach. The obtained results demonstrate the effectiveness of the proposed models in the prediction of WSPCs. [ABSTRACT FROM AUTHOR]

A one‐year's worth of near‐global marine shallow single‐layer cloud top radiative cooling (CTRC) is derived from a radiative transfer model with inputs from the satellite cloud retrievals and reanalysis sounding. The mean cloud top radiative flux divergence is 61 Wm−2, decomposed into the longwave and shortwave components of 73 and −11 W m−2, respectively. Equatorward of 30°N/S, the CTRC is largely a reflection of free‐atmospheric specific humidity distribution: a dry atmosphere enhances CTRC by reducing downward thermal radiation. Consequently, the cooling minimizes in the "wet" tropics and maximizes in the "dry" eastern subtropics. Poleward of 30°N/S, the CTRC decreases slightly due to the colder clouds that emit less effectively. The CTRC exhibits distinctive seasonal cycles with stronger cooling in the winter and has amplitudes of order 10–20 Wm−2 in stratocumulus‐rich regions. The datasets were used to train a machine‐learning model that substantially speeds up the retrieval. Plain Language Summary: Marine low‐lying clouds cool by emitting thermal radiation. The cooling is known as cloud top radiative cooling (CTRC). A change in CTRC can influence the properties of marine clouds via many avenues, ranging from altering the vertical motions of the clouds to changing the clouds' ability to reflect sunlight. Despite the importance of CTRC to the climate system, its climatological characteristics, namely how it varies with space and time, remain unknown. This work fills this knowledge gap. We generate the product of the CTRC over the global ocean using a novel satellite methodology developed in our previous work. Analyses of the data show that the spatial and temporal distributions of the CTRC are largely reflections of the atmospheric humidity: the drier the atmosphere, the stronger the cooling. As a result, the CTRC is weakest in the wet tropics and strongest in the dry eastern subtropical oceans, such as the west of California coast. We also use the CTRC data to train a machine‐learning algorithm that can substantially speed up the calculation of CTRC. Key Points: A one‐year's worth of global marine shallow single‐layer cloud top radiative cooling (CTRC) is derived from satellite and reanalysis dataSpatial and seasonal variations of CTRC are largely reflections of changes in free‐tropospheric humidity and cloud top temperatureA neural network model for the CTRC was trained, which substantially speeds up the retrieval while maintaining good accuracy [ABSTRACT FROM AUTHOR]

Domain-specific architectures of artificial neural networks (ANNs) have been developed to estimate salinity levels for planning at key monitoring stations in the Sacramento-San Joaquin Delta, California. In this work, we propose three major enhancements to existing ANN architectures for purposes of training time reduction, estimation error reduction, and better feature extraction. Specifically, we design a novel multitask ANN architecture with shared hidden layers for joint salinity estimation at multiple stations, achieving a reduction of 90% training and inference time. As another major structural redesign, we replace predetermined preprocessing on input data by a trainable convolution layer. We further enhance the multitask ANN design and training for salinity forecasting. Test results indicate that these enhancements substantially improve the efficiency and expand the capacity of the current salinity modeling ANNs in the Delta. Our enhanced ANN design methodologies have the potential for incorporation into the current modeling practice and provide more robust and timely information to guide water resource planning and management in the Delta. [ABSTRACT FROM AUTHOR]