MOTIVATION: Segmentation of cell bodies and organelles in microscopy images is critical for biological research, particularly in scenarios with multiple regions of interest where spatial continuity is essential. The Mamba architecture, derived from State Space Models (SSMs), has recently gained attention for efficiently modeling long-range dependencies in sequences, achieving excellent results in both natural and medical image segmentation. However, in vision tasks, current Mamba scanning strategies mainly focus on raster-scanning and local-scanning, which introduce spatial discontinuities, severely affecting the effectiveness of segmentation at the pixel level, especially in dense segmentation tasks. RESULTS: In this article, we propose DyMamba, a Mamba-based model featuring a dynamic scanning strategy that adaptively plans scanning paths based on local features and complexity. In addition, to address the challenges of detail prediction and small object detection, we introduce a local aware module that performs pixel-level regional processing on images. DyMamba achieves robust segmentation across diverse microscopy image types, including cell-, organelle- and tissue-scale images. Experiments on six datasets and multiple scanning strategies demonstrate the excellent performance of our method in segmenting microscopy images, achieving an average improvement of 6.9% in mDice and 4.3% in mIoU over state-of-the-art methods across all datasets. AVAILABILITY: The code is released at https://github.com/cbqBit/dymamba.

Ground Penetrating Radar (GPR) is widely used in geotechnical engineering investigations, construction quality assessment, and geological disaster surveys due to its high resolution, accuracy, and non-destructive testing capabilities. However, the accuracy of GPR inversion imaging is often compromised by climatic conditions (such as precipitation and temperature) and complex subsurface environments, leading to suboptimal performance. To address this issue, we propose a Shuffle Attention U-Net Generative Adversarial Network for GPR inversion imaging—SAU-GAN. This network consists of a generator and a discriminator. The generator features an encoder-decoder network enhanced with a Shuffle Attention mechanism, facilitating efficient feature extraction from B-scan images and aiding in the generation of permittivity models. The discriminator evaluates generated models against real ones, providing feedback to supervise the generator’s performance. Both components use double normalization to stabilize parameters and convolutional outputs. Additionally, a multi-scale structural similarity (MS-SSIM) loss function enhances the existing loss function, significantly improving inversion results. Experiments with synthetic data demonstrate that SAU-GAN produces permittivity models with higher accuracy and clearer boundaries than existing methods. Even under interference, it is able to perform precise inversion, demonstrating outstanding robustness and generalization performance. We conduct a quantitative analysis of SAU-GAN using SSIM, PSNR and MSE metrics, further validating its superior performance. When applied to real measured data, SAU-GAN also exhibits commendable performance, validating its effectiveness and practical value.

Traditional ground-penetrating radar (GPR) inversion techniques, while capable of providing high-resolution subsurface imaging, suffer from issues, such as heavy reliance on initial models, high computational demands, and sensitivity to noise and data incompleteness. In contrast, deep-learning-based methods excel in feature extraction and model fitting. However, as a data-driven algorithm, the practical application of convolutional neural networks (CNNs) is limited by the quantity of labeled samples. To reduce the dependence of CNN-based GPR inversion methods on observational data and labels, this project proposes an inversion method based on closed-loop CNNs (CL-CNNs). This approach improves inversion accuracy and reduces the ill-posedness of GPR inversion by modeling both the forward and inverse GPR processes. The CL structure increases the number of features that CNNs can learn from limited labeled samples, while the mutual inversion constraints between the forward and inverse subnetworks help alleviate the ill-posedness of the inversion problem, making the inversion results more consistent with geological principles. Research using synthetic data demonstrates that this method outperforms traditional approaches, as evidenced by enhanced structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR), and a significantly lower mean-squared error (mse), highlighting its advanced performance compared with traditional open-loop CNNs (OL-CNNs). Furthermore, applying this method to real measurement data further validates its effectiveness and practical applicability in engineering contexts, emphasizing its significant practical value.

Electrical resistivity tomography (ERT) is a key geophysical technique that provides detailed information on subsurface structures by measuring the distribution of electrical resistivity underground. ERT suffers from limitations in electrode arrangement, interference from environmental and instrument noise, and existing data processing algorithms that fail to adequately consider geological heterogeneity and uncertainty, resulting in insufficient inversion resolution. Traditional ERT methods rely on simplified algorithms and a limited number of observation points, which smooths model details and further reduces resolution. To address the resolution issues in ERT, this article proposes a deep learning inversion method that integrates prior physical information. This method uses low-resolution inversion results as prior knowledge to provide the deep learning algorithm with a constrained initial model, thereby combining the physical basis of traditional methods with the data-driven advantages of deep learning. The method not only retains the strengths of traditional inversion but also enhances the resolution and imaging efficiency of the inversion model using deep learning technology. Synthetic data experiments demonstrate that integrating deep learning significantly improves the model’s ability to detail subsurface structures, especially in the transition zones of shallow structures and the recovery of deep anomalies. Results from measured data indicate that the proposed method not only achieves high-resolution inversion but also maintains good consistency with prior information.

Due to the influence of both the observation environment and the instruments themselves, ground-penetrating radar (GPR) data are often contaminated by random noise, which degrades data quality. Salt-and-pepper noise is a common type of such noise. Adaptive median filtering is an effective technique for removing this noise. However, it has the drawback of replacing original values that are not affected by noise with the median, which can lead to a degradation in image quality. In this letter, we propose an improved adaptive median filtering method. First, we assess whether the original value is contaminated by salt-and-pepper noise. If the value is affected, filtering is applied. The window size is adaptively increased, and the window is subdivided into smaller sections. Multiple median calculations are then performed on the segmented windows to ensure the validity of the median. When the noise density is high, the median of the nonnoise points in the largest window is selected as the output, thereby minimizing the negative impact of noise on the median calculation. Both synthetic and real-world data validations demonstrate that the improved method significantly outperforms traditional adaptive median filtering, conventional median filtering, and other filtering methods, particularly in high-noise scenarios, thus confirming the superiority of the proposed algorithm.

Ground-penetrating radar (GPR) inversion typically relies on iterative methods, which often involve high computational complexity and challenges in noise handling. These limitations affect the robustness and generalization of traditional approaches. To address these issues, we propose an innovative inversion method using diffusion models (DGPRI-Net) tailored for GPR. Diffusion models inherently capture signal characteristics through progressive noise addition and subtraction, reducing noise impact and enhancing robustness. This approach effectively overcomes the noise management weaknesses of conventional methods. In the reverse generation process, we use the UNet++ network architecture, enhanced with vision transformer (ViT) structures and a simple parameter-free attention module (SimAM). This combination improves multiscale feature extraction and contextual understanding, increasing robustness and enabling high-precision permittivity models. To further evaluate the robustness of the model, we prepared three dedicated test sets: one with added noise, one without low-frequency signals, and one with 30% of the columns missing. Comparative experiments with synthetic data showed exceptional inversion accuracy, superior noise management, and enhanced robustness and generalization. We also validated performance with metrics such as structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean squared error (mse). Applied to measured data, our method continued to yield impressive results, confirming its practical value and effectiveness. This study highlights the potential of diffusion models in advancing GPR inversion applications.

Although deep learning techniques for Ground Penetrating Radar (GPR) inversion offer significant advantages, such as high accuracy and computational efficiency, they still face challenges related to limited robustness and generalization. Using envelope radar data in inversion helps mitigate some of these issues. This data emphasizes amplitude characteristics, reducing the impact of high-frequency noise and phase-related problems on the inversion results. In this paper, we propose a GPR inversion method based on a conditional generative adversarial network (cGAN), incorporating envelope radar data as conditional input for both the generator and discriminator. We also use double normalization to adjust the discriminator’s convergence speed, ensuring the adversarial relationship is maintained, which enhances model robustness without sacrificing inversion accuracy. For the loss function, we introduce a hybrid of Mean Squared Error (MSE) and Multi-Scale Structural Similarity Index Measure (MS-SSIM) loss, guiding the generator to produce results closer to the true model. To evaluate the inversion performance of our proposed method, we designed three synthetic data experiments: a comparison experiments with varying degrees of low-frequency component depletion, a comparison experiments with different noise levels and a comparison experiments with varying central frequencies. Results show high resolution, clear inversion boundaries, and well-defined anomalous structures, demonstrating high inversion accuracy and robustness. Furthermore, our method shows superior performance and practical value with real-world data.

Magnetic anomaly pole transformation converts magnetic field data into an equivalent response at the true magnetic pole, eliminating shifts and distortions, simplifying the interpretation of subsurface magnetic bodies, and improving data interpretation and inversion accuracy. However, the main challenge in magnetic anomaly pole transformation lies in the nonlinear nature of the signals, making traditional methods difficult to apply. The interaction between the shape, depth, and magnetic inclination of magnetic bodies, especially in high- and low-latitude regions, can distort the transformed signal, leading to unclear causal relationships. To address this, this article proposes a deep learning-based approach that automatically extracts high-dimensional features and establishes nonlinear mappings to enhance the correlation between magnetic anomaly signals and geological structures. Deep learning does not require explicit physical models and, through training with large datasets, demonstrates stronger robustness and accuracy, especially in areas where traditional methods fail. The proposed method is validated using both synthetic and measured data. Synthetic data simulates magnetic bodies of various shapes, depths, and magnetic inclinations, confirming the method’s stability and accuracy in handling complex nonlinear signals. The measured data evaluates its pole transformation advantages in typical ore deposit regions. The results indicate that the deep learning model significantly enhances the accuracy of pole transformation, particularly in areas with complex magnetic anomaly signals, effectively preventing signal distortion and demonstrating exceptional generalization capabilities.

Gravity inversion is the pioneer in exploring the structural characteristics of the Earth, the Moon, and other celestial bodies. Classical gravity inversion methods aim to estimate the 3D subsurface density distribution from the observed 2D surface gravity anomalies, which is an ill-posed problem. Constraints can provide vertical resolution and reduce uncertainty. However, these methods significantly increase the cost of data acquisition. This manuscript presents a novel joint inversion method to estimate subsurface density anomaly via a physics-inspired neural network. The observed signals in the proposed method are the gravity anomalies on multiple altitudes and their vertical gradients, which provide vertical resolution for gravity inversion. The proposed joint inversion method contains two stages. The proposed inversion model is initially pre-trained on the synthetic data. Self-supervised transform learning with a closed loop between inversion and forward models is applied to the target gravity anomalies and their gradients. The loss function is defined by mean absolute error, cross-gradient loss, and total variation. Experiments on independently and identically distributed synthetic data, as well as out-of-distribution field data, demonstrate the effectiveness of the proposed method.

In electrical resistivity tomography (ERT), the anomaly effects of different electrode arrays vary depending on the geological model. The appropriate combination of different electrode arrays can optimize detection performance and enhance the reliability of interpretation results. However, traditional inversion methods, constrained by single-array data, sparse observations, and ill-posed problem-solving, often yield low-resolution or inaccurate results. To address the resolution challenges in ERT inversion, inspired by the outstanding fusion and nonlinear mapping capabilities of multi-modal deep learning (DL) image methods, we propose the super-resolution ERT fusion network (SRERTF-Net), which utilizes traditional inversion results of multi-array as the initial models, efficiently leveraging and integrating prior physical information to achieve multi-array data joint super-resolution inversion. In SRERTF-Net, different down-sampling paths are employed to process the inversion results of various electrode arrays, while Inception modules are introduced to enhance feature extraction. Additionally, dense connections are implemented both within and across paths to effectively integrate complementary information from different arrays, ensuring robust multi-modal feature fusion. Finally, we designed training samples that include randomly generated typical structural models and comprehensive complex models, in order to enhance the practicality and adaptability of the network. Experiments on synthetic and field measured data indicate that SRERTF-Net outperforms other methods in terms of resistivity accuracy, resolution, and background performance.

Certain ore deposits feature both density and electrical anomalies, making them detectable via gravity and electromagnetic (EM) methods. However, under complex field conditions, signals are often distorted or lost due to observational errors, undermining inversion reliability. In joint inversion, errors from a single data source may mislead the overall model, resulting in structural deviations and blurred orebody boundaries. Additionally, gravity and EM inversions exhibit different volume effects, often causing inconsistencies in spatial scale representation. Their distinct physical mechanisms further hinder the establishment of clear nonlinear mappings, limiting the effectiveness of traditional joint inversion approaches in achieving consistent integration and stable results. To address these challenges, we propose a Spatial Density-Informed Electromagnetic Inversion Network (SDI-EMI Network), a deep inversion network that fuses spatial density and EM response data. The network first performs gravity inversion to estimate orebody geometry, which serves as a structural prior for guiding EM inversion. By aligning volume deformation patterns, it ensures unified scale representation, enhances data complementarity, and suppresses interference from non-orebody regions. This method overcomes limitations in prior modeling and data fusion while leveraging deep learning’s nonlinear capacity. Experimental results confirm that SDI-EMI Network. offers improved resolution, structural clarity, and robustness for identifying deposits with coexisting density and resistivity anomalies, supporting its potential in complex geological settings.

Magnetic inversion aims to estimate the subsurface susceptibility distribution from surface magnetic anomaly data. Recently, supervised deep learning (DL) methods have been widely utilized in lots of geophysical fields including magnetic inversion. However, these methods rely heavily on synthetic training data, whose performance is limited since the synthetic data is not independently and identically distributed with the field data. Thus, we proposed to realize magnetic inversion by self-supervised learning. The proposed self-supervised knowledge-driven method for 3D magnetic inversion (SSKMI) learns on the target field data by a closed loop of the inversion and forward models. Given that the parameters of the forward model are preset, SSKMI can optimize the inversion model by minimizing the difference between observed and re-estimated surface magnetic anomalies. Besides, there is a knowledge-driven module in the proposed inversion model, which makes the DL-based method more explicable. Meanwhile, comparative experiments demonstrate that the knowledge-driven module can accelerate the training and achieve better results. Since magnetic inversion is an ill-pose task, SSKMI proposed to constrain the inversion model by a guideline from a well log, seismic waves, or electromagnetic signals. The experimental results demonstrate that the proposed method is a reliable magnetic inversion method with outstanding performance.

The acquisition of magnetic anomaly data is generally considered a process of information degradation, with its content significantly impacting subsequent tasks involving data processing, inversion, and interpretation. Traditional interpolation methods often rely on the spatial distribution and sampling density of data, thus struggling to handle complex nonlinear relationships effectively. To address these challenges, this study employs deep learning algorithms for interpolating magnetic anomaly data, aiming to enhance the resolution of magnetic data. Additionally, gravity data is incorporated as supplementary information to improve the quality of magnetic anomaly data interpolation. Similar to magnetic data, gravity data also exhibits a certain degree of spatial correlation, as a single geological source may produce anomalies in both gravity and magnetic responses simultaneously. Through the training and prediction of deep learning networks, it is observed that the intelligent interpolation retains the subtle features of magnetic anomaly data in space while avoiding staircase-like erroneous anomalies generated by linear interpolation. Furthermore, gravity data assists in constraining the results of magnetic anomaly interpolation, enhancing their accuracy. Finally, the trained network is applied to measured data, with the input data being downsampled. The results show that the network can accurately predict magnetic anomaly data and bring them closer to the magnetic anomaly data before downsampling.

Noise attenuation is crucial in ground-penetrating radar (GPR) data processing. In recent years, deep learning (DL) methods have shown excellent performance in GPR denoising tasks, but they typically focus only on recovering the target signal, which can lead to over-denoising. To enhance the generalizability and the practicality of denoising networks, we propose a strategy to generate random dielectric models from natural image datasets, which can quickly construct model datasets with low redundancy and reasonable distribution. To enhance the fidelity of GPR denoising, we leverage the powerful nonlinear fitting capabilities of convolutional neural networks (CNNs) and introduce a closed-loop denoising network framework for GPR. The framework consists of a denoising sub-network and a noise extraction sub-network, effectively achieving signal-noise separation in noised GPR data. Specifically, the denoising sub-network is used to recover weak reflection signals and initially remove noise, while the noise extraction sub-network is used to restore the true noise, mitigating the problem of over-denoising. A key innovation of our approach is the integration of bandpass filtering, which enhances the robustness of network training and supports effective weak signal recovery. This network framework forms a closed loop through the residual loss between the signal-noise separation results and the noised GPR data, the closed-loop structure is capable of further refining the signal and noise prediction results of the two subnetworks, thereby enhancing the numerical accuracy of the signal-to-noise separation results. Finally, the effectiveness of the GPR closed-loop denoising network is verified from multiple perspectives using both synthetic and field measured data. The results indicate that our proposed method is more competitive in GPR denoising tasks.

Three-dimensional inversion of magnetic data can obtain the distribution of subsurface magnetic targets. Deep learning is an effective way to achieve 3-D inversion, which trains a neural network to learn the features of magnetic anomaly data and then generates a 3-D model based on these features. Large training samples are required to achieve persuasive results due to the limited observational data and the multisolution nature of the inverse problem. To reduce the nonuniqueness of the inversion, this article proposes a multivariate magnetic data-based deep learning 3-D inversion strategy. With the proposed strategy, more domain knowledge is incorporated into the training data of the neural network to improve the inversion accuracy. The input data of the neural network adopt multivariate observation data, including multiscale data and multitype data such as magnetic three-component data, magnetic gradient tensor data, and so on, and output a 3-D model to realize 3-D to 3-D mapping. Then, the neural network structure uses the 3-D convolution to extract 3-D spatial information. Both tests on simulation and measured data verify that the proposed strategy can effectively improve the accuracy of the 3-D magnetic inversion.

Quality factor (Q) estimation is critical for the processing of nonstationary seismic data and is an important indicator of oil and gas. Traditional methods for Q value estimation require the identification of the top and bottom of each constant Q layer, which can be challenging in the processing of field seismic data. Deep-learning (DL)-based Q inversion methods leverage the powerful nonlinear fitting capabilities of deep network to automatically obtain interval Q estimates directly from the input seismic data. However, these methods possess so-called “black box” characteristics and lack interpretability, thereby limiting their practical application. To address these issues, this study proposes a physics-driven neural network (PDNN) that integrates physical knowledge with deep neural networks, embedding the frequency-shift method for Q value calculation into the computational layers of the network. Our approach uses nonstationary seismic signals and their corresponding logarithmic time-frequency amplitude spectrum (LTFAS) as input. The neural network decouples the dynamic wavelets and reflection coefficients to obtain the LTFAS of dynamic wavelets. Furthermore, a network layer is designed based on the frequency-shift method to generate the interval Q curve. Experiments on both synthetic and field data demonstrate that the neural network constrained by physical knowledge can alleviate the instability in interval Q calculations, yielding more stable Q estimates. Additionally, this approach enhances the interpretability and generalization capabilities of DL methods, offering significant practical value.

Inversion of seismic data, particularly full waveform inversion (FWI), allows for high-resolution subsurface velocity estimation. However, the inversion of subsurface velocities using only seismic data typically involves severe non-uniqueness. Electromagnetic exploration, due to its broad detection range and low cost, can effectively complement seismic exploration. Although electromagnetic data give a lower resolution resistivity information, they are sensitive to subsurface anomalies. Hence, the joint inversion of electromagnetic and seismic data effectively integrates the complementary information in both data sets to reduce the inversion non-uniqueness to improve accuracy and reliability of the inversion results. Nevertheless, current joint inversion techniques are confronted with issues such as the complexity of objective function design, challenges in achieving convergence, and insufficient coupling between seismic and electromagnetic data. To address these challenges, we propose a Seismic-Electromagnetic joint Inversion Network (SEMI Net) based on joint learning. Our approach leverages the powerful nonlinear fitting capabilities of neural networks for efficient multi-objective optimization. Moreover, we establish coupling between seismic and electromagnetic data across multiple sampling scales. Harnessing the frequency band complementarity of seismic and electromagnetic data, i.e. the low-frequency of the electromagnetic data and the mid-to-high frequency of the seismic data, we obtain high-resolution resistivity and velocity models by SEMI Net. Results on synthetic data and the Overthrust model demonstrate the effectiveness of our approach.

The difficulty of 3D magnetic inversion is to use 2D magnetic anomaly data to obtain 3D magnetic susceptibility structure. The contribution of the underground medium to the magnetic anomaly decreases rapidly with the increase of the depth, which leads to the rapid attenuation of the inversion resolution with the depth. In this paper, artificial intelligence (AI) technology is applied to 3D magnetic inversion to predict the susceptibility model corresponding to magnetic anomaly. The inversion network built in this paper uses the method of down-sampling in the encoder to increase the receptive field and realize the feature extraction of magnetic anomaly data. In the decoder, attention fusion modules are added to fuse feature maps from different sources. Finally, we added a 3D refiner behind the decoder. The 3D refiner converts the 2D feature map from the decoder into 3D data. Based on the typical complex medium theory, this paper constructs a diverse sample set of complex 3D susceptibility models. The inversion experiment of synthetic data verifies the feasibility and versatility of the proposed network. Compared with the other methods, the distribution of susceptibility prediction obtained by our method is more accurate and more reliable in determining the magnetic body boundary. In the field example of Jinchuan Copper-nickel sulfide deposit in China, the network constructed in this paper can achieve high-precision 3D underground susceptibility imaging in this area. The susceptibility distribution is in good agreement with the borehole data and the proved deposit distribution.

Magnetotelluric (MT) inversion constitutes a pivotal research domain within the purview of electromagnetic data interpretation, characterized by its inherent nonlinearity and illposed problem. Traditional MT inversion algorithms often require introducing an initial model as a prior constraint, and then drawing the electrical distribution of the structure based on the observed data, which has limitations such as low computational efficiency and high computational costs. This paper proposes an efficient and high-quality MT intelligent joint inversion method based on artificial intelligence (AI) control strategy to address the issues in MT inversion problems. Capitalizing on the strong nonlinear fitting capabilities of convolutional neural networks (CNNs), the closed-loop network composed of forward and inversion subnetworks is constructed to enable the closed-loop network to train in the absence of labels, thereby solving the restrictive problem of the small number of label samples faced by MT inversion. Simultaneously, the reciprocal constraint between forward and inversion subnetworks can suppress inversion multiplicity, leading to improved inversion accuracy. In addition, the uncertainty in inversion can be further reduced by mutual constraints between apparent resistivity and phase data. Finally, this paper tests and verifies the effectiveness of the closed-loop network using synthetic and measured data. The results demonstrate that the closed-loop network significantly enhances the depth resolution of inversion and elevates the reliability of inversion results. Moreover, the closed-loop network can also effectively predict the apparent resistivity and phase response data that are close to those simulated via the finite element method.

The traditional method to improve the resolution in electromagnetic inversion is increasing the number of iterations, which displays poor non-linear mapping and strong non-uniqueness. To meet this challenge, a new strategy is proposed via reconstructing the geoelectric model for traitional inversion results through a deep neural networks (DNN). DNN possesses the advantage on establishing an uncertain mapping between low-resolution images and high-resolution target images. In order to recover the high-precision geoelectric model, we propose an end-to-end electromagnetic recovery network (EMRNet) with novel components to adequately utilize the geoelectric model data of traditional inversion. Specifically, EMRNet uses the codec structure from U-Net, whereby a cross-scale feature attention module (CSFA Block) is incorporated into the decoding process to make full use of feature information of different scales. The superiority of EMRNet are validated on both synthetic and measured data, The predicted geoelectric models of EMRNet are more consistent with the target from the aspects of resistivity values, overall structure, and resolution. In addition, the geoelectric model predicted by EMRNet agree well with the real geological background data and corresponding response data is closer to the measured data.

The gravity method is one of the non-destructive geophysical methods, which aims to estimate the 3D subsurface density distribution of geological bodies from the observed 2D surface gravity anomalies. Recently, deep learning has achieved great success in solving ill-posed problems including gravity inversion. The limitation of the current deep learning methods for gravity inversion is the difference between synthetic and field data. Thus, we introduce a self-supervised estimation method for 3D gravity inversion (SSGI). SSGI learns the field data directly by closed-loop of the inversion model and forward model. The proposed inversion model contains an encoder, an expander, a decoder, and a 3D refiner. Since the forward model is built according to the law of universal gravitation, SSGI can optimize the inversion model by minimizing the mean absolute error of the original and reconstructed gravity anomalies. Besides, SSGI constrains the inversion model by a guide-line in the auxiliary loop. Since the guide-line corresponds to the sampling or average of the density matrix, minimizing the mean absolute error between the original guide-line and the generated guide-line can reduce the uncertainty of inversion. The experimental results demonstrate that the proposed SSGI achieves state-of-the-art performance in 3D gravity inversion.

The problem of China's coastal water pollution is becoming more and more serious. Thus the management of sewage outfall with technology methods is gaining more attention. WebGIS technology such as ArcGIS Server, ArcGIS Online and Flex, etc. may be of great help. We can establish a B/S structure system with the advantages of easy building up and function expansion, high information sharing and strong user interaction capability, low cost, and so on. The approach to design and Implement a Sewage Outfall Management System based on WebGIS technology is discussed in this article. The results show that this system can better manage the outfall information and provide support to decision-making than traditional ones, and it also has broad application prospects in other fields of environmental protection. This system has been applied to our project so far and just went through the interim review.

Support vector machine (SVM) and its derivative algorithms have been increasingly used to predict algal blooms recently. However, its computation complexity remains an annoying problem. To improve the time cost of SVM, a hybrid approach is proposed in this paper based on Partial Least Square (PLS) feature extraction and Core Vector Machine Regression (CVR) algorithm. We describe the principle of our algorithm and the implementation steps in detail. Based on the biweekly data gathered from Tolo Harbour, Hong Kong, some comparative analysis of the performance of PLS-CVR and other algorithms are presented. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably. The experimental results show that our algorithm can reduce the time cost significantly compared to conventional SVM algorithm while maintaining a satisfactory accuracy.

Machine learning (ML) techniques such as artificial neural network (ANN) and support vector machine (SVM) have been increasingly used to predict harmful algal blooms (HABs). In this paper, we use the biweekly data in Tolo Harbour, Hong Kong, and choose several machine learning methods to develop prediction models of algal blooms. Three different kinds of models are designed based on back-propagation (BP) neural network, generalized regression neural network (GRNN) and support vector machine (SVM) respectively. The experimental results show that the improved BP algorithm and SVM work better than GRNN methods, and the models based on SVM present the best performance in terms of goodness-of-fit measures, but need to be further improved in the running time. We develop these prediction models with different lead time (7-day and 14-day) to study further. The results indicate that the use of biweekly data can simulate the general trend of algal biomass reasonably, but it is not ideally suited for exact predictions. The use of higher frequency data may improve the accuracy of the predictions.