63 results about "Terrain classification" patented technology

The embodiment of the invention provides an AI analysis method and system applied to land resource investigation, and the method comprises the steps: obtaining the multi-source remote sensing image data of a target region, analyzing the spectrum and texture features of the multi-source remote sensing image data, generating a comprehensive land feature map, obtaining an initial land classification map through a land classification model, and obtaining the initial land classification map. And performing time sequence comparative analysis in combination with historical data to determine a dynamic area of land coverage change and generate a land resource change report. The method further comprises the functions of soil quality evaluation, change area visualization, abnormal area real-time monitoring, land classification model training, classification model adjustment according to user feedback and the like, and the efficiency and accuracy of land resource investigation are improved.

The invention discloses a terrain classification method based on a random forest algorithm, a server and a storage medium, and belongs to the technical field of terrain classification, and the method comprises the steps: obtaining DEM data and remote sensing image data of a research area, and extracting terrain features, spectral features, index features and texture features; designing a feature combination scheme, and generating an optimal feature combination data set as input data of terrain classification; calculating an optimal parameter combination of a random forest algorithm, and generating an optimal random forest classifier; establishing a terrain classification system suitable for the research area, and constructing a terrain classification training sample set and a verification sample set; performing terrain classification by using a random forest classifier and the optimal feature combination data set to obtain a terrain classification result of the research area; and calculating and evaluating the precision of a terrain classification result by utilizing a verification sample set and a Kappa coefficient evaluation method. By adopting the method, the terrain classification refinement degree and the terrain classification calculation efficiency and classification efficiency can be improved.

The invention discloses a spectrum-space depth fusion hyperspectral image classification method oriented to a small sample condition, and the method comprises the steps: firstly carrying out the multi-scale hole convolution processing of input hyperspectral data through a range attention convolution SAC module, and extracting the multi-scale context features; then, a spatial normalization attention SNA mechanism is utilized to carry out adaptive weighting adjustment of spatial dimensions on the feature map, and spatial feature representation of the key area is enhanced; the method comprises the following steps: constructing a lightweight hybrid expert model LMOE, carrying out parallel processing and gating weighting through a multi-path expert network, carrying out efficient refining and mapping on features, finally fusing processed spectral features and spatial features, and carrying out pixel-level prediction through a classifier to obtain a terrain classification result map of a hyperspectral image. The method solves the problems that in the prior art, overfitting is prone to occurring under the small sample condition, the spectrum-space collaborative modeling capacity is insufficient, the long-range dependence obtaining efficiency is low, and the recognition precision is reduced under the class imbalance scene.

The invention discloses an idle land recognition method, device and equipment and a storage medium, and belongs to the technical field of machine learning, and the method comprises the steps: obtaining a first land feature set based on the land text data of a plurality of pieces of land; screening the first land feature set to obtain a second land feature set; semi-supervised learning cooperative training is carried out on a first classifier and a second classifier based on the second land feature set, the first classifier and the second classifier are used for idle land identification, the first classifier is realized based on a rotating forest algorithm, and the second classifier is realized based on an extreme gradient lifting algorithm; wherein in the semi-supervised learning cooperative training process, the prediction labels are screened based on a preset idle land classification rule. The method can realize accurate and efficient idle land identification.

The invention provides a quadruped robot terrain classification method and system based on sound feature and IMU information fusion, and relates to the field of terrain perception, and the method specifically comprises the steps: collecting an audio signal, processing the audio signal to obtain a Mel spectrogram, collecting an IMU signal, drawing a triaxial IMU signal time domain graph, dividing the time domain graph into a plurality of IMU information graphs, and obtaining a quadruped robot terrain classification result. The feature value of each time domain graph window is calculated, an IMU information graph corresponding to the Mel-frequency spectrogram in time relation can be established, then the Mel-frequency spectrogram and the IMU information graph are spliced according to the time sequence, and the spliced Mel-frequency spectrogram and IMU information graph are input into a neural network model for terrain classification; according to the method, the classification capability of the unstructured outdoor terrain is enhanced in a mode of fusing sound perception and ontology perception, and the spliced input image not only contains audio features, but also retains IMU information, so that a neural network model can learn from two different perception sources, terrain features can be comprehensively understood, and the classification efficiency of the unstructured outdoor terrain is improved. Therefore, the recognition capability of the complex environment is improved.

The invention discloses a terrain classification and self-adaptive gait switching method for a sea-turtle-imitating amphibious robot based on visual tactile perception. The method comprises the following steps: establishing a robot model carrying a pressure sensor and a waterproof camera; the method comprises the following steps: acquiring tactile time sequence data and visual images in various terrains such as broken stones and sand beaches, and constructing a training data set; establishing a multi-modal convolutional neural network, and fusing features through dual-channel feature extraction and an adaptive attention mechanism; motion energy consumption and speed under each terrain are collected, and the optimal gait is determined; and constructing a gait switching finite-state machine in combination with terrain physical attribute classification and the optimal gait. According to the method, a sensing-decision-driving closed loop is formed, the terrain is sensed through visual touch, the gait is decided through a finite-state machine, the gait driving motion is executed, and through experimental verification, the self-adaptive gait can improve the moving efficiency and speed, and the amphibious environment adaptability of the robot is enhanced.

The application provides a terrain recognition method, device and equipment for a legged robot and a medium. The method comprises the following steps: acquiring pose data collected by a body sensor of a target legged robot, and calculating state parameters of the target legged robot; inputting the pose data and the state parameters into a foot-ground contact force model to determine three-dimensional foot-ground contact force; generating a contact mask sequence representing a foot-end ground contact state of the target legged robot based on the three-dimensional foot-ground contact force, and segmenting the pose data to obtain a segmented pose data set; inputting the pose data set into a pre-trained terrain classification model to determine a confidence vector of the current ground under multiple terrain attributes, and determining a terrain category of the current ground according to multiple confidence vectors. Through the method and device, the autonomous adaptation capability and environmental perception reliability of the legged robot in complex terrain are significantly improved.

The invention discloses a power transmission line microtopography recognition method, system and device based on a multi-modal attention mechanism and a medium, and the method comprises the steps: obtaining power transmission line space corridor data and multi-source geographic information data of a target region, carrying out the preprocessing, and generating a line feature map and a multi-channel ground feature map; constructing a double-branch deep learning network model, inputting the line feature map into a topographic feature branch, inputting the multi-channel ground feature map into a line feature branch, and respectively extracting deep feature information; through a cross-branch space attention module, weighting the features extracted by the topographic feature branches by using a space attention weight generated by extracting the features by the line feature branches to obtain fusion features; and inputting the fused features into a decoder for up-sampling and feature reconstruction, and outputting a classification recognition result for the microtopography along the power transmission line corridor. According to the invention, full-process intelligent processing is realized, the identification efficiency is obviously improved, and the method is suitable for wide-range line census and risk assessment.

The invention discloses a terrain classification method and system based on a plate capacitance tomography sensor, and belongs to the technical field of robot application. The method comprises the following steps: firstly, designing a panel ECT sensor array comprising eight electrodes, and building a mobile robot body sensing data acquisition system, so as to acquire mutual capacitance data and inertial data of terrains; and secondly, performing feature extraction, fusion and classification on the capacitance data and the inertial data by building a multi-layer perceptron classification model, after the multi-layer perceptron model is trained, moving the robot to a known terrain, and completing the classification of the terrain through the multi-layer perceptron. According to the method, two kinds of modal information of inertia and capacitance are utilized, through combination of interactive vibration information of the robot and the terrains and changes of dielectric constants in the terrains, the terrains made of various different materials can be automatically recognized, and the method has high universality and practical significance.

This application proposes a method, device, and electronic device for acquiring fire rescue tactics. The method includes: acquiring the fire line classification features and terrain classification features of any first grid cell in a set of grid cells corresponding to the fire line; acquiring the feature vector corresponding to any first grid cell based on the first feature vector corresponding to the fire line classification features, the second feature vector corresponding to the terrain classification features, and the third feature vector corresponding to the environmental features of any first grid cell; and analyzing the feature vector corresponding to any first grid cell using a target graph convolutional neural network model and a target temporal Transformer model to acquire the fire rescue tactics corresponding to any first grid cell. This solves the technical problem in the prior art that fire relief methods cannot adapt to changes, resulting in a mismatch between the fire relief methods and the current fire situation, and poor fire extinguishing effect.

The application discloses a passability prediction method for unstructured terrain environment, and is applied to the technical field of vehicle traffic capacity prediction. The application constructs a three-dimensional grid map based on a multi-source sensor, combines semantic terrain classification and elevation structure feature extraction, and forms a geometric and semantic joint representation of the environment. By comparing the control instruction with the motion response of the vehicle in the actual terrain, a traffic capacity coefficient is constructed as a supervision signal, and a conditional variational autoencoder is trained to model the traffic capacity coefficient distribution and its uncertainty under different terrain conditions. Further, intermediate layer features are extracted from the semantic recognition network to establish a category statistical model for identifying terrain categories that do not appear in the training set. Finally, the traffic capacity confidence and terrain reliability are fused to output a grid-level traffic probability, realizing the passability prediction in the unstructured environment.

The application provides a topographic classification method and system based on typhoon wind speed prediction, and the method comprises the following steps: obtaining topographic basic data of a classification area; constructing a topographic multi-classification set based on regional meteorological model wind speed and the topographic basic data; obtaining typhoon wind speed prediction data and performing risk assessment on the topographic multi-classification set to obtain a risk index grading classification result corresponding to the topography; and obtaining a microtopography distribution map of the classification area based on the risk index grading classification result. The topographic classification method based on typhoon wind speed prediction can realize the risk assessment of a future line under a current wind speed environment by adding the typhoon wind speed prediction data into the topographic classification process for dynamic topographic classification, and the line operation and scheduling efficiency is improved.

The application relates to a photovoltaic pile driver terrain identification method and system, a terminal and a storage medium, and relates to the technical field of intelligent equipment. The application comprises the following steps: analyzing camera pixel data, laser radar data, a camera internal parameter matrix, a camera radar external parameter matrix and a radar satellite external parameter matrix to determine pixel camera data, radar world data and camera world data; fusing the radar world data and the camera world data to determine simultaneous space fusion data and coordinate point gray values; analyzing the coordinate point gray values and the simultaneous space fusion data to determine spatial terrain features and pile driving terrain classification; and analyzing the radar world data, the camera world data, the spatial terrain features and the pile driving terrain classification to determine real-time terrain modeling. The application has the effects of improving the photovoltaic pile driving terrain identification accuracy and terrain updating efficiency.

The land classification method based on deep and shallow feature and multi-attention mechanism fusion, electronic equipment and storage medium belong to the technical field of remote sensing image processing and deep learning semantic segmentation. In order to solve the problem of accurate classification of similar categories and category boundaries in land use classification method, the present application comprises the following steps: constructing a multi-scale normalized spatial attention mechanism layer; constructing a multi-scale normalized direction position attention mechanism layer; constructing a multi-scale normalized channel attention mechanism layer; constructing a land use classification network model, including a normalized multi-attention feature extraction network and a multi-deep and shallow feature fusion dilated convolution network; collecting data in the city-rural area adaptive land surface cover dataset; performing data preprocessing to obtain land data set; inputting the land data set into the constructed land use classification network model; performing land classification training based on deep and shallow feature and multi-attention mechanism fusion; and obtaining the land classification model based on deep and shallow feature and multi-attention mechanism fusion. The present application is accurate in classification.

The invention relates to the technical field of railway line selection, and discloses a terrain-driven intelligent site selection and line trend search method for a long and large mountain railway tunnel. The search method integrates three parts of topographic data preprocessing, topographic classification and tunnel portal search and route trend search, and specifically comprises the following steps: converting three-dimensional topographic data into spatial waves, and processing the spatial waves through Fourier transform to obtain topographic relief features; a low-pass filter is designed based on the representative tunnel length for filtering preprocessing; carrying out block classification on a topographic region by utilizing the processed topographic data, and searching a feasible tunnel portal position; grouping the potential tunnel portal positions; the construction cost is used as a main consideration factor, a bidirectional A algorithm is used for connecting tunnel portals, a plurality of feasible schemes are generated, and an optimal path is obtained through comparison and selection; and generating a specific three-dimensional railway line scheme by using the optimal path. The searching method is high in searching efficiency and suitable for railway line design in complex mountainous areas.

The application discloses a remote sensing image-based paleontological fossil producing area identification method and system, and belongs to the field of paleontological fossil exploration, comprising the following steps: acquiring GF-2 multispectral images, digital elevation data and geological map data, pre-processing the GF-2 multispectral images to obtain a land classification map, performing Gram-Schmidt fusion processing on a panchromatic band and a multispectral band, performing vectorization processing on the geological map data and extracting Qiongzhusi group stratum boundaries, and obtaining a slope factor map based on the digital elevation data; superimposing the Qiongzhusi group stratum boundary vector data on the fused GF-2 multispectral images, and extracting an image candidate area of the Qiongzhusi group stratum through spatial analysis. The application realizes efficient and accurate fossil exploration through multi-source data fusion and intelligent analysis technology, and effectively solves the problem of fossil feature identification in mixed pixels by combining a dynamic spectral decoupling algorithm with a professional fossil spectral feature library and solving component abundance through constraint energy minimization.

A land use classification method based on remote sensing images, comprising the following steps: step 1: obtaining the original image of remote sensing images of urban and township land, marking the pictures after screening, and a total of four categories: cultivated land, orchard, forest and building; step 2: cutting and data enhancement are carried out on the marked pictures to make a data set; step 3: the training set in the prepared data set is put into a feature extraction network and a multi-scale fusion up-sampling structure for training to obtain a segmentation model; step 4: the remote sensing image to be classified is put into the trained segmentation model to obtain the result; the purpose of the present application is to solve the problems of low small target precision and poor edge segmentation effect when the land categories are many and the features are complex in the land classification method based on remote sensing images, and a method capable of realizing more accurate identification result of various land types in remote sensing images is proposed.

The present invention relates to a system, vehicle, model training method, model, program for processing image data IMG(1) to identify a traversable region TRR of an off-road terrain ORT. The traversable region TRR represents a region TRR of the off-road terrain ORT which is traversable by a vehicle. The system includes one or more processor collectively configured to receive image data representing an image IMG(1) of the off-road terrain (ORT). The image data is captured by at least one imaging sensor provided on the vehicle. The image data is processed using an image segmentation model to segment the image IMG(1) into a plurality of image segments, the plurality of image segments identifying one or more terrain feature TF(1), TF(2) present in the off-road terrain ORT. In dependence on an operational capability of the vehicle, each of the one or more identified terrain features TF(1), TF(2) is classified as either being traversable, TTR or non-traversable (Figure 4B not shown, UTR). The traversable region TRR of the off-road terrain ORT is determined by identifying a region of the image IMG(1) which excludes any terrain features TF(1), TF(2) classified as being non-traversable (UTR). The system outputs traversable terrain data, TTD representing the traversable region TRR of the off-road terrain ORT.

This invention discloses a spatial downscaling method for weather forecast models based on a terrain classification super-resolution model. The method first obtains nested data at each layer through dynamic downscaling of the weather forecast model, then trains a super-resolution model using data at different resolutions of the target area. Following the above process, super-resolution models are established for different terrains, and a terrain classification model is trained using terrain data. The super-resolution model and the terrain classification model are combined to obtain a fused prediction result. This fused model is the SRBTC model, which considers spatial correlation. The model outputs the similarity probability between the area to be predicted and various different terrains. The prediction results are weighted and summed to obtain the fused prediction result of the super-resolution model. During model training, a specified value scaling method is proposed to consider the differences between high- and low-resolution meteorological simulation data. When applying the model, a splitting and merging method is proposed to consider the differences between multi-scale simulation data, and the applicability of the model is specified.

The invention belongs to the technical field of remote sensing, and provides a surface feature classification method based on combination of a hyperspectral image and a laser radar. The method comprises the following steps: acquiring a hyperspectral image and laser radar data; labeling ground feature categories in the processed data, and making a label file; constructing a ground object target detection model and performing training according to the data and the label file; the model comprises a multi-scale spectrum-space sparse coding module used for extracting hyperspectral sparse features, a multi-scale geometric structure sparse coding module used for extracting laser radar geometric sparse features, and a competitive sparse selection module used for multi-scale feature dynamic fusion. And finally, utilizing the trained model to generate a ground feature classification result. According to the terrain classification method, the problem of neglecting modal sparsity difference and feature redundancy is solved through a'modal specificity sparse modeling + multi-scale competitive selection 'mechanism, and the accuracy and robustness of multi-source remote sensing data terrain classification in a complex scene are effectively improved.

The present invention discloses a method for controlling the motion of a quadruped robot in a complex environment based on a pulse neural network. First, a single-leg model of the quadruped robot and a conversion model of its leg joint angle state and foot end position coordinates are constructed. Subsequently, a hybrid neural network is constructed and trained. A terrain classification result is obtained using the trained hybrid neural network. Finally, the quadruped robot gait planning and foot end trajectory planning are completed based on the terrain classification result. The leg joint angle is output from the foot end target position to complete the motion control of the quadruped robot. The technical solution of the present invention utilizes image recognition based on a pulse neural network to classify the robot's environment. The recognition accuracy is high and the hardware requirements are low. In addition, appropriate gait planning and motion control decisions are made for the quadruped robot according to different environment types, so that the quadruped robot has stronger adaptability and passability in complex terrains with different characteristics, and has certain promotion value.

The invention discloses an ancient biological fossil producing area identification method and system based on a remote sensing image, and belongs to the field of ancient biological fossil exploration, and the method comprises the steps: obtaining a GF-2 multi-spectral image, digital elevation data and geological map data, carrying out the preprocessing of the GF-2 multi-spectral image, obtaining a land classification map, carrying out the Gram-Schmi dt fusion processing of a panchromatic wave band and a multi-spectral wave band, and obtaining a land classification map; vectorizing the geological map data, extracting the stratum boundary of the qiongzhuea temple group, and obtaining a slope factor graph based on the digital elevation data; and superposing the boundary vector data of the qiongzhuea tumidinoda temple formation to the fused GF-2 multispectral image, and extracting an image candidate region of the qiongzhuea tumidinoda temple formation through spatial analysis. According to the method, efficient and accurate fossil exploration is achieved through the multi-source data fusion and intelligent analysis technology, the dynamic spectrum decoupling algorithm is combined with a professional fossil spectrum feature library, the component abundance is solved through constraint energy minimization, and the problem of fossil feature recognition in mixed pixels is effectively solved.

The invention relates to the technical field of land management, in particular to a land utilization optimization method and system based on big data, which are used for acquiring soil, climate, infrastructure and terrain data, classifying according to soil particles, drainage and nutrients, analyzing road and water conservancy distribution, evaluating accessibility and supply capability and generating a land utilization feature classification result. According to the method, the soil quality, the climate conditions, the infrastructure distribution and the terrain and landform information are comprehensively analyzed, so that the land classification precision is improved, and the comprehensiveness of data integration is ensured. According to soil characteristics and long-term environmental changes, land use matching is optimized, and the scientificity of resource allocation is improved. In combination with terrain change characteristics, drainage conditions are optimized, the water accumulation risk is reduced, and the land applicability is enhanced. The spatial distribution is adjusted, the use intensity is optimized, use conflicts are reduced, and the rationality of the land spatial structure is improved.

The invention provides a PolSAR ground feature classification method based on a cross-channel attention mechanism, and mainly solves the problems of limited feature extraction capability and weak generalization performance of a classification model under the condition of sample scarcity. According to the scheme, the method comprises the following steps: 1) carrying out preprocessing including Pauli decomposition and sliding window operation on a PolSAR image; 2) the preprocessed image is divided into training and test data sets; (3) a hybrid network model based on Unet and ViT is constructed, the hybrid network model comprises an input layer, two processing branches composed of coders and decoders and an output layer, and an encoder is the combination of a ViT encoder and a Unet encoder; 4) performing parameter optimization training on the model by using the training set; and 5) inputting test data into the trained model to realize PolSAR terrain classification. According to the method, local, global and multi-scale features of the PolSAR image can be effectively captured, and the classification precision is improved.

The invention provides a terrain recognition method, device and equipment for a foot-type robot and a medium, and the method comprises the steps: obtaining pose data collected by a body sensor of a target foot-type robot, and calculating state parameters of the target foot-type robot; inputting the pose data and the state parameters into a foot-ground contact force model, and determining a three-dimensional foot-ground contact force; based on the three-dimensional foot-ground contact force, generating a contact mask sequence representing the ground contact state of the foot end of the target foot type robot, and segmenting the pose data to obtain a segmented pose data set; and inputting the pose data set into a pre-trained terrain classification model, determining confidence vectors of the current ground under various terrain attributes, and determining the terrain category of the current ground according to the multiple confidence vectors. By means of the method and device, the self-adaptive capacity and environment sensing reliability of the foot type robot under the complex terrain are remarkably improved.

The invention discloses a communication optical cable external damage prevention monitoring method and system suitable for a changing environment, and relates to the technical field of communication optical cables, and the method comprises the steps: obtaining a communication optical cable planning map and a high-precision map of a to-be-planned region, and unifying the communication optical cable planning map and the high-precision map to the same coordinate through a coordinate conversion tool; identifying terrain junctions through spatial analysis and generating a terrain classification map; and dynamically adjusting the installation attitude of the sensing optical cable according to the environmental data of the terrain, collecting vibration data, and judging the external damage risk of the communication optical cable through a neural network. According to the method, key monitoring is carried out on a high-risk area, the monitoring efficiency is improved, and potential risks are found in time; different sensing optical cable mounting postures are selected according to the characteristics of different terrains, so that the monitoring precision is improved; different sensing optical cable installation postures are selected according to environment data at junctions of different terrains, targeted monitoring is achieved, and communication optical cable external damage prevention monitoring efficiency and accuracy are improved.

The present invention provides a method for batch extracting small reservoirs based on high-precision remote sensing imagery. The method comprises collecting multiple years of high-resolution land classification data, and further comprises the following steps: fusing the high-resolution land classification data; utilizing multi-source data to remove polygons representing areas without reservoirs from water body polygons; capturing and annotating images from high-definition remote sensing imagery using water body polygons; and constructing and training a machine learning image classification model to classify the images. The method proposed in the present invention batch extracts small reservoirs based on high-precision remote sensing imagery. The method integrates multiple years of high-resolution land use data, utilizes environmental data such as water surface frequency and land use, vegetation, and population distribution to exclude water body data such as lakes and rivers, and utilizes a machine learning image recognition algorithm to extract small reservoirs from a large number of water surfaces.

A method for terrain classification and adaptive gait switching of a turtle-like amphibious robot based on visual-haptic perception, comprising: establishing a robot model equipped with a pressure sensor and a waterproof camera; collecting haptic time series data and visual images on various terrains such as gravel and sand, and constructing a training data set; establishing a multi-modal convolutional neural network, fusing features through double-channel feature extraction and adaptive attention mechanism; collecting motion energy consumption and speed on each terrain to determine the optimal gait; combining terrain physical property classification and optimal gait to construct a gait switching finite state machine. The method forms a "perception-decision-driving" closed loop, which perceives the terrain through visual-haptic perception, decides the gait through the finite state machine, and executes the gait driving motion. Experimental verification shows that the adaptive gait can improve the mobility efficiency and speed, and enhance the adaptability of the robot in amphibious environment.

Systems and methods for real-time terrain classification via an autonomous robot are provided. An example method may obtain environmental data indicating one or more characteristics of a physical environment including terrain, and classify the terrain based upon the environmental data. Based upon a first classification of the terrain, the method may determine a terrain assessment task associated with reclassifying at least the portion of the terrain and determine whether performing the terrain assessment task will exceed a performance threshold indicating an adverse effect on performing a mission task. Based upon determining whether performing the terrain assessment task will exceed the performance threshold, the method may generate terrain assessment task configuration data for configuring the autonomous robot and transmit the terrain assessment task configuration data to the autonomous robot causing configuration of the autonomous robot associated with the terrain assessment task.

The invention belongs to the technical field of weather forecast, and discloses a rainfall downscaling method and device and a medium, and the method comprises the steps: obtaining a low-resolution rainfall forecast field of a target region, and carrying out the upsampling to obtain a basic high-resolution rainfall forecast field; obtaining topographic features of the target area, dividing topographic types for the target area according to the topographic features, and generating topographic classification masks corresponding to the topographic types; fusing the topographic features and the topographic classification mask to obtain a topographic feature map; fusing the topographic feature map and the basic high-resolution rainfall forecast field, and inputting the fused topographic feature map and basic high-resolution rainfall forecast field into a topographic perception variational auto-encoder to obtain a hidden space vector; denoising the hidden space vector by using a diffusion model under the condition of the topographic feature map; and reconstructing the denoised hidden space vector into a high-resolution rainfall forecast field by using a variational self-decoder. The topographic features are fused into the downscaling process, depth utilization of topographic information is achieved in the downscaling process, and the problem that a traditional method is insufficient in rainfall detail depicting capacity of a complex topographic area is solved.