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Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

The quality of traffic services depends on the accuracy and completeness of the collected traffic data. However,the existing traffic data imputation methods usually only rely on the predefined road network structure to capture the spatio-temporal features and only consider the imputation effect from a single perspective, which are very limited for imputation of different missing patterns of road traffic data. In this paper, we propose a novel deep learning framework called Hierarchical Spatio-temporal Graph Convolutional Neural Networks(HSTGCN) to impute traffic data,through the macro layer and the road layer. The model constructs macro graph of the road network based on the data temporal correlation clustering, which can mine the temporal dependencies of road traffic data from a hierarchical perspective. Besides, a temporal attention mechanism and adaptive adjacency matrix are introduced in the road layer to better extract the spatio-temporal information of the road traffic data. Finally, we use graph convolution neural networks to learn the spatio-temporal feature representations of the road layer and macro layer, which are then fused to achieve data imputation. To illustrate the efficient performance of the model, experiments are conducted on traffic data collected from California and Seattle. The proposed model performs better than the comparison model for traffic data imputation. • A full dynamic graph is built based on the urban road structure. • Extracting spatio-temporal features from multiple dimensions using hierarchical thinking. • Gate-GCN perform effective feature selection. [ABSTRACT FROM AUTHOR]

There are an estimated 800,000 small reservoirs globally with a range of uses. Given the collective importance of these reservoirs to water resource management and wider society, it is essential that we can monitor and understand the hydrological dynamics of ungauged reservoirs, particularly in a changing climate. However, unlike large reservoirs, continuous and systematic hydrological observations of small reservoirs are often unavailable. In response, this study has developed a retrieval framework for water levels of small reservoirs using a deep learning algorithm and remotely sensed satellite data. Demonstrated at four reservoirs in California, satellite imagery from both Sentinel-1 and Sentinel-2 along with corresponding water level field measurements was collected. Post-processed images were fed into a water level inversion convolutional neural network model for water level inversion, while different combinations of these satellite images, sampling approaches for training/testing data, and attention modules were used to train the model and evaluated for accuracy. The results show that random sampling of training data coupled with Sentinel-2 satellite imagery was generally the most accurate initially. Performance is improved by incorporating a channel attention mechanism, with the average R2 increasing by 8.6% and the average RMSE and MAE decreasing by 15.5% and 36.4%, respectively. The proposed framework was further validated on three additional reservoirs in different regions. In conclusion, the retrieval framework proposed in this study provides a stable and accurate methodology for water level estimation of small reservoirs and can be a powerful tool for small reservoir monitoring over large spatial scales. [ABSTRACT FROM AUTHOR]

Simple Summary: Earthquakes are a common occurrence at The Geysers geothermal field in California which, over the years, have led to general land sinking in the area. In our study, we explore the correlation of geothermal data and seismicity and find that the injection rate exhibits a high correlation with the number of earthquakes. Additionally, we rely on satellite imagery which measures the extent of the land subsidence and develop a machine learning model that predicts future land subsidence, finding that it has a relatively low error rate. Moreover, we incorporate geothermal data into this model and find that it performs even better. All in all, this encourages the use of machine learning models in hazard mitigation in order to minimize the potential impacts that land subsidence might bring. The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations. [ABSTRACT FROM AUTHOR]

Modeling the spread of wildland fires is essential for assessing and managing fire risks. However, this task remains challenging due to the partially stochastic nature of fire behavior and the limited availability of observational data with high spatial and temporal resolutions. Herein, we propose an attention-based deep learning modeling approach that can be used to learn the complex behaviors of wildfires across different fire-prone regions. We integrate optimized spatial and channel attention modules with a convolutional neural network (CNN) modeling architecture and train the attention-based fire spread models using a recently derived fire-tracking satellite observational dataset in conjunction with corresponding fuel, terrain, and weather conditions. The evaluation results and their comparison with benchmark models, such as a deeper and more complex autoencoder model and the semi-empirical FARSITE fire behavior model, demonstrate the effectiveness of the attention-based models. These new data-driven fire spread models exhibit promising modeling performances in both the next-step prediction (i.e., predicting fire progression from one timestep earlier) and recursive prediction (i.e., recursively predicting final fire perimeters from initial ignition points) of observed large wildfires in California, and they provide a foundation for further practical applications including short-term active fire spread prediction and long-term fire risk assessment. [ABSTRACT FROM AUTHOR]

Temporary roads are often placed in mountainous regions for logging purposes but then never decommissioned and removed. These abandoned forest roads often have unwanted environmental consequences. They can lead to altered hydrological regimes, excess erosion, and mass wasting events. These events can affect sediment budgets in streams, with negative consequences for anadromous fish populations. Maps of these roads are frequently non-existent; therefore, methods need to be created to identify and locate these roads for decommissioning. Abandoned logging roads in the Point Reyes National Seashore in California, an area partially under heavy forest canopy, were mapped using object-based image processing in concert with machine learning. High-resolution Q1 LiDAR point clouds from 2019 were used to create a bare earth model of the region, from which a slope model was derived. This slope model was then subjected to segmentation algorithms to identify and isolate regions of differing slopes. Regions of differing slopes were then used in a convolutional neural network (CNN), and a maximum likelihood classifier was used to delineate the historic road network. The accuracy assessment was conducted using historic aerial photos of the state of the region post-logging, along with ground surveys to verify the presence of logging roads in areas of question. This method was successfully able to identify road networks with a precision of 0.991 and an accuracy of 0.992. It was also found that the CNN was able to identify areas of highest disturbance to the slope gradient. This methodology is a valuable tool for decision makers who need to identify areas of high disturbance in order to mitigate adverse effects. [ABSTRACT FROM AUTHOR]

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels. [ABSTRACT FROM AUTHOR]

Sustainable management of groundwater resources under changing climatic conditions require an application of reliable and accurate predictions of groundwater levels. Mechanistic multi-scale, multi-physics simulation models are often too hard to use for this purpose, especially for groundwater managers who do not have access to the complex compute resources and data. Therefore, we analyzed the applicability and performance of four modern deep learning computational models for predictions of groundwater levels. We compare three methods for optimizing the models' hyperparameters, including two surrogate model-based algorithms and a random sampling method. The models were tested using predictions of the groundwater level in Butte County, California, USA, taking into account the temporal variability of streamflow, precipitation, and ambient temperature. Our numerical study shows that the optimization of the hyperparameters can lead to reasonably accurate performance of all models (root mean squared errors of groundwater predictions of 2 meters or less), but the "simplest" network, namely a multilayer perceptron (MLP) performs overall better for learning and predicting groundwater data than the more advanced long short-term memory or convolutional neural networks in terms of prediction accuracy and time-to-solution, making the MLP a suitable candidate for groundwater prediction. [ABSTRACT FROM AUTHOR]