Comment on hess-2022-403

An essential step in surface wave exploration is the inversion of dispersion curves. By inverting dispersion curves, we can effectively establish the shear-wave velocity model and obtain reliable subsurface stratigraphic information. The inversion of dispersion curves is an inversion problem with multiple parameters and multiple poles, and obtaining a high precision solution is difficult. Among the methods of inversion of dispersion curves, local search methods are prone to fall into local extremes, and global search methods such as particle swarm optimization (PSO) and genetic algorithm (GA) present the disadvantages of slow convergence speed and low precision. Deep learning models with strong nonlinear mapping capability can effectively solve nonlinear problems. Therefore, we propose a method called PSO-optimized long short-term memory (LSTM) network (PSO-LSTM) to invert the dispersion curves in order to improve the effect of inversion of dispersion curves. The method is based on the LSTM network, and PSO is used to optimize the LSTM network structure and other parameters that need to be given manually to improve the prediction of the network. Two theoretical geological models are used in the paper: Model A and Model B to test the PSO-LSTM. The tests include the noisy data test and noise-free data test. Model A was tested without noise, and Model B was tested with noise. In addition, PSO and LSTM were tested on model A to compare the performance of PSO-LSTM. In Model A, the maximum relative errors of PSO and LSTM are 20.76% and 5.85%, respectively, and the maximum standard deviations of PSO and LSTM are 57.37 and 1.97, respectively. For PSO-LSTM, the maximum relative errors of Model A and Model B in the inverse results are 2.05% and 2.09%, and the maximum standard deviations of Model A and Model B in the inverse results are 1.23 and 3.87, respectively. The test results of Model A show that the inversion performance of PSO-LSTM is better than those of LSTM and PSO, and the performance of the network can be improved after PSO is used to optimize the network parameters. The inverse results from Model B show that the PSO-LSTM is robust and can invert the dispersion curves well even after adding noise to the model. Finally, the PSO-LSTM is used to invert the actual data from Wyoming, USA, which demonstrates that the PSO-LSTM can be used for the quantitative interpretation of Rayleigh wave dispersion curves.

Interpretable deep learning model for building energy consumption prediction based on attention mechanism

<p>Flooding is one of the most financially devastating natural hazards in the world. Studying storage-discharge relations can have the potential to improve existing flood forecasting systems, which are based on rainfall-runoff models. This presentation will assess the non-linear relation between daily water storage (ΔS) and discharge (Q) simulated by physical-based hydrological models at the Rum River Watershed, a HUC8 watershed in Minnesota, between 1995-2015, by training Long Short-Term Memory (LSTM) networks and other machine learning (ML) algorithms. Currently, linear regression models do not adequately represent the relationship between the simulated total ΔS and total Q at the HUC-8 watershed (R<sup>2</sup> = 0.3667). Since ML algorithms have been used for predicting the outputs that represent arbitrary non-linear functions between predictors and predictands, they will be used for improving the accuracy of the non-linear relation of the storage-discharge dynamics. This research will mainly use LSTM networks, the time-series deep learning neural network that has already been used for predicting rainfall-runoff relations. The LSTM network will be trained to evaluate the storage-discharge relationship by comparing two sets of non-linear hydrological variables simulated by the semi-distributed Hydrological Simulated Program-Fortran (HSPF): the relationship between the simulated discharges and input hydrological variables at selected HUC-8 watersheds, including air temperatures, cloud covers, dew points, potential evapotranspiration, precipitations, solar radiations, wind speeds, and total water storage, and the dynamics between simulated discharge and input variables that do not include the total water storage. The result of this research will lay the foundation for assessing the accuracy of downscaled storage-discharge dynamics by applying similar methods to evaluate the storage-discharge dynamics at small-scaled, HUC-12 watersheds. Furthermore, its results have the potentials for us to evaluate whether downscaling of storage-discharge dynamics at the HUC-12 watershed can improve the accuracy of predicting discharge by comparing the result from the HUC-8 and the HUC-12 watersheds.</p>

Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea

<p dir="ltr">Recurrent Neural Networks (RNNs) are a major class of Artificial Neural Networks (ANNs). Suitable network architecture is vital for RNNs to achieve high performance. However, designing the architecture of deep RNNs (DRNNs) is a complicated and time-consuming process. Each layer of a DRNN contains numerous hyper-parameters. It is challenging to optimize the hyper-parameters of all layers jointly to achieve the best possible learning performance. Trial-and-error methods for designing DRNNs have been proven to be laborious and highly costly in practice. Therefore, an efficient automatic architecture search technique is needed to design DRNNs.</p><p dir="ltr">Genetic Algorithm (GA) is a popular evolutionary computation based approach. Existing research works already explored the use of GAs in designing ANNs. But, most of the existing approaches focus on the design of fixed-depth ANNs. However, it is not easy to fix the depth of DRNNs in advance, since different problems need DRNNs of different depths. Hence, the potential of GA has to be further explored to evolve the architecture of DRNNs of varying depths.</p><p dir="ltr">The primary goal of this thesis is to develop advanced GA approaches for designing Long Short Term Memory (LSTM) based DRNN architectures of varying depths effectively and efficiently.</p><p dir="ltr">Firstly, this thesis proposes a GA-based algorithm called Two-Stage Surrogate-Assisted GA (TS-SA-GA) with a progressive incremental strategy to design the architecture of LSTM networks of varying depths. The progressive approach can effectively extend well-designed shallow networks to high performing deep networks. Moreover, the new GA-based algorithm adopts newly designed knowledge-driven crossover and mutation operators to identify and repair LSTM network designs affected by inappropriate use of activation functions, thereby significantly reducing the chances for GA to evolve hard-to-train LSTM network architectures. Furthermore, this thesis proposes a two-stage surrogate method to predict the trainability and fitness of LSTM networks. This improves the efficiency and effectiveness of the GA-based algorithm to evolve LSTM networks.</p><p dir="ltr">Secondly, this thesis proposes a new algorithm called Evolutionary Design of LSTM Ensembles (ED-LSTM-Ensemble) based on multi-objective optimization techniques to evolve LSTM networks and ensembles simultaneously to directly utilize the ensemble performance to drive the evolution of base LSTM networks. Additionally, this thesis proposes a connection weight inheritance strategy to evolve LSTM networks and ensembles efficiently and effectively.</p><p dir="ltr">Thirdly, this thesis proposes a new algorithm called Skip Connections through Evolutionary and Differential Architecture search (SCEDA) to design LSTM networks together with appropriate skip connections automatically. Designing LSTM networks together with skip connections is highly challenging. This thesis hybridize gradient-based DAS and evolutionary architecture search to optimize the architecture of LSTM networks with suitable skip connections in a single evolutionary process to improve the efficiency and effectiveness of the search process.</p><p dir="ltr">Finally, this thesis conducts an in-depth empirical analysis of the impact of activation functions on the trainability of LSTM networks based on the concept of Edge Of Chaos. The analysis reveals the strong interrelation between the activation functions across multiple layers of an LSTM network and the trainability of the network. The analysis also highlights the importance of controlling activation functions to avoid generating untrainable LSTM networks. On the basis of the analysis, this thesis proposes a machine learning model to guide the use of activation functions in LSTM networks.</p>

Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states’ signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners.

With the rapid increase in bridge spans, the mitigation of risk to flutter (aeroelastic instability) is of critical importance in the design of long-span bridges, especially considering the more frequent intense hurricanes under climate change. Although the strong nonlinearities of the aeroelastic (self-excited) forces in wind–bridge interactions can be well captured through either numerical simulations or experimental tests, both are expensive and time consuming. Hence, it is important to develop an efficient reduced-order model for the simulations of nonlinear aeroelastic forces on the bridge decks. This study proposes a reduced-order model based on the long short-term memory (LSTM) network to simulate the nonlinear aeroelastic forces on bridge decks with various leading edges, and thus rapidly predict the corresponding post-flutter behaviors of long-span bridges. To generate the training datasets, computational fluid dynamics (CFD) was employed to simulate the nonlinear aeroelasticities of bridge decks with a wide range of leading-edge configurations and wind speeds. Trained on the high-fidelity CFD datasets, the LSTM network takes the motion of a bridge deck, leading-edge angles and wind speeds as inputs and outputs the nonlinear aeroelastic forces on the bridge decks. A hybrid loss function utilizing the prediction errors of both aeroelastic forces simulated by the LSTM network and the bridge deck responses calculated by the Newmark-β algorithm was introduced into the training process to improve the network performance. The prediction results of the trained LSTM model were compared with the CFD simulations, which demonstrated that the nonlinear aeroelastic forces of the bridge deck with various leading edges can be accurately and efficiently acquired by the proposed LSTM model.

To address the issues of slow convergence speed, poor dynamic adaptability, and path redundancy in the Double Deep Q Network (DDQN) within complex obstacle environments, this paper proposes an enhanced algorithm within the deep reinforcement learning framework. This algorithm, termed LPDDQN, integrates Prioritized Experience Replay (PER) and the Long Short Term Memory (LSTM) network to improve upon the DDQN algorithm. First, Prioritized Experience Replay (PER) is utilized to prioritize experience data and optimize storage and sampling operations through the SumTree structure, rather than the conventional experience queue. Second, the LSTM network is introduced to enhance the dynamic adaptability of the DDQN algorithm. Owing to the introduction of the LSTM model, the experience samples must be sliced and populated. The performance of the proposed LPDDQN algorithm is compared with five other path planning algorithms in both static and dynamic environments. Simulation analysis shows that in a static environment, LPDDQN demonstrates significant improvements over traditional DDQN in terms of convergence, number of moving steps, success rate, and number of turns, with respective improvements of 24.07%, 17.49%, 37.73%, and 61.54%. In dynamic and complex environments, the success rates of all algorithms, except TLD3 and the LPDDQN, decreased significantly. Further analysis reveals that the LPDDQN outperforms the TLD3 by 18.87%, 2.41%, and 39.02% in terms of moving steps, success rate, and number of turns, respectively.

This study evaluates the performance of traditional statistical methods, specifically the Seasonal Autoregressive Integrated Moving Average (SARIMA), in comparison to advanced deep learning models—Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN)—for wind speed forecasting across various regions in Saudi Arabia: Al-Jouf, Abha, Al-Ahsa, and Al-Dawadami. The historical wind speed dataset covering the year 2018 was utilized, with data preprocessing conducted using the Exploratory Data Analysis (EDA) method to ensure consistency and quality. The models were assessed based on three primary error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). Results show that CNN consistently outperforms SARIMA and the other deep learning models, particularly when forecasting stable wind speed patterns. LSTM demonstrates an ability to handle fluctuating wind speeds effectively, while BiLSTM offers advantages in capturing complex bidirectional temporal dependencies. On the other hand, SARIMA generally exhibits lower performance compared to deep learning approaches. The superior performance of CNN is likely attributed to its strength in local feature extraction and handling spatial patterns effectively, which are beneficial for short-term forecasting. These findings provide valuable insights into model selection for wind energy forecasting and can contribute to optimizing renewable energy integration and planning strategies. Future work may explore hybrid model approaches to further enhance forecasting accuracy.

This project focuses on improving the detection of network attacks by using a machine learning technique known as Long Short-Term Memory (LSTM) networks. LSTM networks are a type of neural network that excels at analyzing sequences of data, making them well-suited for identifying patterns associated with network intrusions. To enhance the LSTM model's effectiveness, we introduce new probability features that help the model better distinguish between normal and malicious activities. Our approach includes collecting network data, preprocessing it to make it suitable for training, and then using this data to train the LSTM model. We evaluate the model's performance using a range of metrics to ensure its accuracy and reliability. The results indicate that our method significantly improves the detection rate of network attacks while also reducing the number of false alarms. This means that our LSTM-based model not only catches more real threats but also makes fewer mistakes in identifying normal activities as attacks. Overall, this project showcases the potential of advanced machine learning techniques, like LSTM networks, to enhance cyber security measures and protect against network threats more effectively.

In order to enhance the accuracy of thermal response estimation, this study proposes the utilization of a deep learning-driven method, specifically the Long Short-Term Memory (LSTM) network, for predicting temperature-induced strains and deflections in the Queensferry Crossing, UK. Initially, an examination of the temperature fields of the bridge is conducted based on monitored temperature data. Subsequently, the LSTM network is introduced as a tool to estimate thermal response utilizing temperature field information. The study proceeds to discuss the results of temperature-induced response estimation, including strains and deflections, obtained through the LSTM network, with a comparative analysis against a linear regression model. It is observed that the temperature discrepancies among steel components within the bridge are lesser in comparison to those of concrete components. The LSTM network exhibits higher estimation accuracy than the linear model, manifesting in an improvement of mean square error from 4.45 to 0.89 for strain estimation. In addition, the LSTM network can partially address time-lag effects. However, the estimation performance of the LSTM network is influenced by the structural response type and the resolution of employed sensors. Specifically, the correlation coefficient (R) associated with strain estimation is measured at 0.980 using the LSTM network, whereas R for deflection estimation stands at 0.825. Generally, local structural responses, such as strains, outperform global responses like deflections. Furthermore, sensors with higher resolutions yield enhanced estimation accuracy.

This paper aims to predict the occurrence of pests and diseases for cotton based on long short term memory (LSTM) network. First, the problem of occurrence of pests and diseases was formulated as time series prediction. Then LSTM was adopted to solve the problem. LSTM is a special kind of recurrent neutral network (RNN), which introduces gate mechanism to prevent the vanished or exploding gradient problem. It has been shown good performance in solving time series problem and can handle the long-term dependency problem, as mentioned in many literatures. The experimental results showed that LSTM performed good on the prediction of occurrence of pests and diseases in cotton fields, and yielded an Area Under the Curve (AUC) of 0.97. The paper further verified that the weather factors indeed have strong impact on the occurrence of pests and diseases, and the LSTM network has great advantage on solving the long-term dependency problem.

Time-history responses of the bridge induced by the moving vehicle provide crucial information for bridge design, operation, maintenance, etc. As inspired by this, this work attempts to provide a new paradigm for vehicle–bridge interaction (VBI) by highlighting the comparison of different deep learning algorithms applied to the prediction of time-history responses of the bridge under vehicular loads. Particularly, three deep learning architectures with few and measurable input features developed by using fully-connected feedforward neural network, long short-term memory (LSTM) network, and convolutional neural network (CNN) are proposed on the basis of the governing equation of bridge vibrations. Three VBI systems with various vehicle models are developed and further validated to produce reliable training data. To examine the accuracy of the predictive models, two advanced metrics are exploited for time-history estimate. Moreover, the proposed deep learning models are comprehensively investigated through a parametric study on the influential factors associated with the VBI system and network architecture. The results show that deep feedforward neural network (DFNN), LSTM network, and CNN can be applied in VBI analysis to estimate the bridge time-history response. The three neural networks have comparable prediction accuracies. When considering the irregularity excitation, CNN is found to be the most efficient predictive model, while DFNN needs the least training time under perfect bridge surface condition.

Adaptive Cruise Control (ACC) is an important part of automotive autonomy. To have a robust Adaptive Cruise Control system, a well-defined sensor fusion system is required. This paper focuses on feature-level sensor fusion system where three fusion techniques are explored: independent Kalman filtering, interacting multiple model (IMM) filtering, and a novel hybrid technique using a combination of both an IMM Filter and deep neural network (DNN). Kalman Filtering is a popular approach for automotive sensor fusion, but heavily relies on the given kinematic models to make state estimations. We explore the IMM filter to capture multi-model motion as well as a DNN approach to build a data-driven model of the system. Our IMM filter is built with several Kalman Filters featuring different kinematic models. The proposed DNN used is a Long Short-Term Memory (LSTM) network trained on radar and camera data to forecast a vehicle's state. To overcome the weaknesses in both IMM filtering and LSTM networks, we propose a hybrid technique of consisting of both the IMM filter and the LSTM network. The proposed hybrid system uses a single filter for each sensor. The filtered sensor data is synchronized and used as the input for a trained LSTM network. Our LSTM network was trained on over 100 simulated highway driving scenarios that might occur in an ACC application. Simulations and code were developed using MATLAB and the Autonomous Driving Toolbox. Our hybrid IMM-LSTM system outperformed the independent Kalman Filtering approach in longitudinal and lateral tracking accuracy (RMSE) by 23 percent and had promising results compared to the IMM filter where the tracking error was decreased by nearly 50 percent in certain driving scenarios.

Flash floods occur frequently and distribute widely in mountainous areas because of complex geographic and geomorphic conditions and various climate types. Effective flash flood forecasting with useful lead times remains a challenge due to its high burstiness and short response time. Recently, machine learning has led to substantial changes across many areas of study. In hydrology, the advent of novel machine learning methods has started to encourage novel applications or substantially improve old ones. This study aims to establish a discharge forecasting model based on Long Short-Term Memory (LSTM) networks for flash flood forecasting in mountainous catchments. The proposed LSTM flood forecasting (LSTM-FF) model is composed of T multivariate single-step LSTM networks and takes spatial and temporal dynamics information of observed and forecast rainfall and early discharge as inputs. The case study in Anhe revealed that the proposed models can effectively predict flash floods, especially the qualified rates (the ratio of the number of qualified events to the total number of flood events) of large flood events are above 94.7% at 1–5 h lead time and range from 84.2% to 89.5% at 6–10 h lead-time. For the large flood simulation, the small flood events can help the LSTM-FF model to explore a better rainfall-runoff relationship. The impact analysis of weights in the LSTM network structures shows that the discharge input plays a more obvious role in the 1-h LSTM network and the effect decreases with the lead-time. Meanwhile, in the adjacent lead-time, the LSTM networks explored a similar relationship between input and output. The study provides a new approach for flash flood forecasting and the highly accurate forecast contributes to prepare for and mitigate disasters.