2247results about "Rainfall/precipitation gauges" patented technology

In some cases, flood risk quantification computing systems and methods are computationally intensive and in some cases are inaccurate. A flood risk computing system and method are provided that quantifies a flood vulnerability within a specified area of interest, irrespective of flood event characteristics; estimates and maps a flood hazard probability; integrates the flood vulnerability and the flood hazard probability to quantify a flood risk; and develops a rapid flood risk software tool, stored in the memory, to directly quantify flood risk characteristics using deep learning. The rapid flood risk took using a plurality of hierarchical deep neural networks.

The invention relates to a rainfall forecast-based landslide surface scene deformation intelligent monitoring method and system. The method comprises the steps of determining a landslide rainfall risk level through multi-index coupling calculation; selecting and implementing a monitoring scheme based on the landslide rainfall risk level; the rainfall intensity is calculated according to the intelligent grid forecast data, a risk grade-rainfall intensity two-dimensional regulation and control matrix is established in combination with the landslide rainfall risk grade, and the risk grade and the rainfall intensity are mapped into regulation and control parameters in a cross mode, so that tracking dynamic adjustment of the monitoring frequency of deformation and rainfall is achieved; and constructing a rainfall-landslide meteorological risk index model, and when the rainfall-landslide meteorological risk index model exceeds a preset risk threshold, generating and issuing an early warning message. According to the invention, through combination of the multi-dimensional risk assessment model and the dynamic regulation and control matrix and integration of time-space coupling analysis, monitoring frequency adaptive optimization and multi-dimensional early warning linkage are realized, a landslide risk accurate perception-intelligent regulation and control-closed loop response full-chain prevention and control system is formed, and the active disaster prevention and control capability is significantly improved.

The invention relates to the field of water quality evaluation, in particular to a rain source type river water quality evaluation method and system.The method comprises the steps that a sensor of a water quality monitoring system monitors micro-rain detection data of a micro-rain event, and the water quality monitoring system adjusts the sampling frequency of the sensor through an adaptive algorithm; obtaining water quality monitoring data through a micro-pollution mutation detection method; constructing a water quality recovery model of the micro-rain event through nonlinear kinetic analysis, and predicting a peak value and recovery time of water quality change of the micro-rain event; identifying a significant sudden change point of water quality change through a chaos theory algorithm, and adjusting the sampling frequency of the sensor through fuzzy logic control; key pollutants are obtained according to the peak value of the water quality change and the recovery time, and the key pollutants and pollution source data are subjected to data correlation analysis to judge the pollution source. The problems that the impact and the influence of a micro-rain event on the river water quality are not fully considered, so that the water quality evaluation is not timely and accurate enough, and the water quality change cannot be effectively captured and a pollution source cannot be analyzed are solved.

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

The purpose of the present invention is to provide a snow cover amount estimation system and a snow cover amount estimation method which enable accurate, economical, and remote measurement of snow cover amount.　A snow cover amount estimation system 301 according to the present invention is provided with: an optical fiber 50 which receives vibration from the surface of the snow at a given point; a vibration measuring instrument 11 for measuring the vibration received by the optical fiber 50 as a distribution in the longitudinal direction of the optical fiber 50; and an analysis processing unit 12 that has, as accumulation data, the relationship between snow cover amount and the vibration received by the optical fiber 50, compares the distribution measured by the vibration measuring instrument 11, as measurement data, with the accumulation data, and estimates the snow cover amount at the given point.

The invention relates to a rainfall kinetic energy space-time prediction method based on dual-polarization weather radar observation parameters, and the method comprises the steps: firstly processing minute-resolution raindrop spectrometer data, calculating raindrop diameter distribution, and calibrating the data of a rain gauge; secondly, dual-polarization radar parameters (ZH, ZDR and KDP) are calculated based on a physical formula, rainfall kinetic energy KE is calculated in combination with micro-physical characteristics, and rainfall types are marked; and then, after atmospheric field variables are extracted and physical normalization is carried out, a bidirectional time series kinetic energy prediction network (BTKE-Net) is constructed, and rainfall kinetic energy is accurately predicted through BiLSTM and a deep regression network. And finally, after attenuation and offset correction, inputting the data into the trained BTKE-Net model, and carrying out real-time space-time prediction. The innovation of the invention lies in that the application potential of the dual-polarization weather radar in the water environment field is excavated, large-range high-precision rainfall kinetic energy space-time prediction is realized, and more accurate scientific basis and decision support are provided for water and soil conservation and meteorological disaster prevention and control.

The invention discloses a multi-source rainfall forecast fusion method based on typhoon similarity, and relates to the field of rainfall forecast, and the method comprises the steps: selecting key factors affecting the formation and development of typhoons, obtaining a multi-dimensional vector corresponding to each typhoon path point for a current typhoon and each historical typhoon, and guaranteeing the comprehensive description of typhoon features; according to the method, a historical typhoon data set is constructed, and a comprehensive correction coefficient of each forecasting mechanism is obtained based on the set and a one-dimensional vector corresponding to each forecasting mechanism, so that the system error of each forecasting mechanism is effectively corrected; meanwhile, the optimal weight coefficient and the standard deviation are calculated through the comprehensive correction coefficient, the latest future rainfall forecast numerical value of each forecasting mechanism is obtained, the comprehensive correction coefficient is used for correction, the forecast correction value corresponding to each forecasting mechanism is obtained, and the forecasting mechanism is obtained by combining the optimal weight coefficient, the standard deviation and the forecast correction value of each forecasting mechanism. The prediction fusion value and the corresponding variance estimation value are obtained, and the prediction precision is greatly improved.

The invention belongs to the technical field of rainfall prediction, and discloses a short and temporary rainfall prediction method based on multi-model random scheduling integration, which develops a robust training and pushing framework based on a continuous rolling prediction strategy, and decomposes long-sequence prediction into manageable stages. According to the method, training is carried out through teacher forcing and planned sampling, error propagation is relieved, and the training process is stabilized. The invention further designs asymmetric encoder-decoders (DSE and AFD) that achieve lower FLOPs than competitive baselines under standardized assessment, where DSE selectively compresses significant features and AFD stepwise reconstructs details to mitigate excessive smoothing problems. Finally, an intensity weighted Gaussian KL divergence loss function is designed, and the key problem of data balance is solved by modeling and predicting on a distribution level and endowing a large weight to a meteorological important heavy rainfall event.

The invention discloses a slope stability prediction method based on deformation monitoring, and relates to the field of slope monitoring, and the method comprises the steps: obtaining a time sequence deformation field of a target slope through employing an InSAR; generating a time sequence Dn through differential interference processing; collecting rainfall data; generating a time sequence representing regional rainfall conditions; grey correlation analysis is adopted, and the correlation degree of Dn and Pn is calculated; constructing a slope slip prediction model based on InSAR displacement and rainfall factors; if the displacement time sequence has an acceleration trend or fluctuation, marking the corresponding slope unit as an early warning analysis object; obtaining rainfall forecast data in a future period of time, and calculating a displacement predicted value di '(N + 1) of the marked side slope unit at the (N + 1) th time point in the future; and comparing the displacement predicted value di '(N + 1) of the (N + 1) th time point with a threshold value, and if di' (N + 1) exceeds the threshold value, giving a slippage early warning level. Aiming at low precision of a rainfall-induced slope slippage early warning model in the prior art, the precision is improved.

The invention provides an urban road ponding monitoring and drainage system. The urban road ponding monitoring and drainage system comprises a monitoring module which collects ponding depth, water velocity, rainfall intensity and field image data in real time; the initialization configuration module sets parameters such as an accumulated water depth threshold value; the data transmission module encrypts and transmits data to the central control module; the model algorithm optimization module constructs a three-dimensional ponding diffusion model and outputs a predicted ponding depth and an optimized drainage strategy; the central control module generates a drainage pump rotating speed instruction and the like; the drainage execution module dynamically adjusts drainage pump power and a drainage path; the self-checking and fault-tolerant module calculates a health index of drainage equipment and switches redundant equipment; the early warning module sends risk level information; and the interaction module displays information such as a real-time accumulated water thermodynamic diagram. The urban road drainage efficiency can be improved, the water accumulation risk is reduced, and smooth urban traffic and public travel safety are guaranteed.

The invention discloses a remote sensing dynamic monitoring method for water and soil loss in water conservancy, and relates to the technical field of combination of water conservancy projects and remote sensing technologies. A data collection module is used for collecting and preprocessing monitoring data of remote sensing images, weather and soil, extracting historical data stored in a storage module, and combining the current monitoring data to obtain a remote sensing result; the method comprises the steps that a soil erosion intensity index TQ, a water and soil loss rate change rate LS and a water and soil conservation measure adjustment factor BCT are substituted into a comprehensive treatment evaluation module, the adjustment direction and strength of a water and soil conservation measure are formulated based on the water and soil conservation measure adjustment factor BCT, and monitoring data and measures for adjusting the direction and strength are stored in a storage module. According to the method, by introducing scientific quantitative indexes and constructing a cyclic influence mechanism, comprehensive monitoring and quantitative analysis of dynamic changes of water and soil loss are realized, and the method not only solves the problems and defects in the prior art, but also provides scientific basis and decision support for formulation and adjustment of water and soil conservation measures.

The invention discloses a water conservancy water regimen monitoring management system based on Beidou short message communication, which relates to the technical field of water regimen monitoring and comprises a scheduling module, a data compression module, a data preprocessing module and an early warning module. The scheduling module determines a transmission queue by marking a water level state, calculating various scores and correction parameters, and implements a preemption mechanism and setting optimal transmission in a silent period; the data compression module classifies and processes data, adjusts the compression rate according to channel signals, and adopts forward error correction coding and the like to guarantee the data accuracy; the data preprocessing module filters the original data, detects abnormity by using space-time correlation analysis, and fills missing values; the system effectively realizes water conservancy water regimen monitoring management, has significant advantages in the aspects of data transmission, processing, early warning and the like, improves the accuracy and timeliness of water regimen monitoring, and provides powerful support for water conservancy management.

The invention relates to the technical field of building construction, in particular to a foundation pit dewatering prediction method based on a digital twinborn technology, which comprises the following steps of: 1, establishing a multi-source data fusion layer; 2, establishing a digital twin modeling layer; 3, establishing an intelligent prediction algorithm layer; 4, establishing a dynamic optimization decision layer, and generating a precipitation well optimization scheme based on a prediction result; according to the method, engineering geological data of a geological survey report before excavation of the building foundation pit, historical meteorology of a building location and hydrogeological data are utilized, digital twinning is carried out on precipitation during the whole construction period of the proposed building foundation pit on the basis of a digital twinning technology, the precipitation period, daily precipitation and influence of precipitation on foundation pit supporting are predicted, and the foundation pit supporting effect is improved. According to the method, the influence of settlement deformation of surrounding buildings is reduced, digital simulation is provided for smooth construction of a whole project, and measurement and prediction are provided for the project and the surrounding environment, so that the construction cost is greatly reduced.

A computer-implemented method includes executing, via a flood simulation model, a computer simulation that simulates flooding within a target spatial area based on an input of a geospatial dataset and the rainfall intensity data of one or more flooding events associated with the target spatial area; determining normalized inundation scores for a set of geographical cells and one or more clusters of interconnected geographical cells based on executing the computer simulation; determining, via an optimization algorithm, a set of optimal sensor locations in the target spatial area based on the normalized inundation scores and the one or more clusters of interconnected geographical cells; and generating a sensor location map that includes the set of geographical cells and a set of geospatial markers identifying the set of sensor locations; and generating a visual output that displays the sensor location map.

The present invention discloses a flooding water level monitoring method and system for power facilities, including after receiving a flooding emergency response notification and a heavy rainfall defense alarm, starting facility flooding water level monitoring; and based on the individual area and total area of facilities affected by flooding and water level sensing monitoring information, establishing a neural network measuring an affected range, and outputting the monitoring result of a flooded surface height in a region, thereby achieving all-weather real-time monitoring, and accurately measuring and timely reporting a flooding height.

The invention discloses a rainfall prediction method and system for the Beijing, Tianjin and Hebei region, and relates to the technical field of rainfall prediction, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the data, extracting the core characteristics of the data, carrying out the dynamic rainfall modeling according to the core characteristics of the data, and simulating a rainfall evolution path; and constructing a rainfall prediction model, performing rainfall intensity prediction based on the rainfall evolution path, performing extreme weather early warning based on a prediction result, and generating a rainfall distribution diagram of a high-risk area. Through fusion of multi-source data, the generative adversarial network and spectral analysis, the spatial resolution of the data and the rainfall evolution prediction capability are improved, the real-time performance and the accuracy of rainfall prediction are improved in combination with dynamic rainfall modeling, a fractional order differential equation and a multiple regression model, and the prediction accuracy is improved. The rainfall prediction challenge under complex weather conditions can be effectively dealt with, and accurate prediction of rainfall intensity and extreme weather events is realized.

The invention relates to the field of flood forecasting, and discloses a flood forecasting method and system, a storage medium and computing equipment, and the method comprises the steps: topological modeling: taking a river catchment area surrounded by a water diversion line and a catchment area outlet hydrometric station as nodes of a graph, constructing a connection relation of edges according to the actual flow direction of a river and the connection relation of branches, and carrying out the topological modeling; therefore, topological graph structure data adaptive to a hydrological mechanism is constructed; converting month number characteristics, generating a dynamic periodic timestamp vector, splicing the dynamic periodic timestamp vector with topological graph structure data, and inputting an attention neural network for training; the attention neural network architecture comprises two independent feature channels which are used for processing historical data and forecast data respectively, the network supports a variable modular architecture, and a flood forecast result can be output according to needs after a model is trained. According to the method, the flood prediction precision and interpretability are improved, and a high-precision and interpretable space-time joint flood prediction scheme is realized.

The invention relates to an earth and rockfill dam seepage safety monitoring, forecasting and early warning system and method, and belongs to the technical field of dam safety monitoring and artificial intelligence, and the system comprises a data acquisition module, a dam safety monitoring and forecasting model module, a dam safety early warning analysis module, and an early warning setting and pushing module. On the basis of a statistical model, engineering data such as geological exploration, detection and test are collected and analyzed according to rainwater conditions and dam safety monitoring data, the equivalent reservoir water level of a leakage channel is deduced, a seepage pressure monitoring and forecasting model considering the lagging time of the leakage channel is established, seepage pressure four-level monitoring and early warning indexes are constructed, and the seepage pressure monitoring and early warning efficiency is improved. On-line early warning of dam seepage safety is achieved, and the safety state of the dam is evaluated in real time.

The invention discloses a small watershed flood early warning method based on an X-band dual-polarization phased array radar, and relates to the technical field of flood early warning, and the method comprises the following steps: obtaining an X-band dual-polarization phased array radar data set and a rainfall station actual measurement rainfall data set, and building a radar rainfall calculation model through linear fitting; acquiring an X-band dual-polarization phased array radar image set, and performing prediction by adopting a short-term rainfall prediction model to obtain a predicted radar image and corresponding predicted radar data; inputting the predicted radar data into a radar rainfall calculation model to obtain predicted rainfall; and carrying out small watershed flood early warning by adopting the flood forecasting model. According to the method, the X-band dual-polarization phased array radar data and image are combined with the radar rainfall capacity calculation model, the short-term rainfall forecasting model and the flood forecasting model, so that the accuracy of rainfall capacity forecasting is improved, and the reliability of flood early warning is further improved.

The invention provides a rainfall inversion method integrating GNSS (Global Navigation Satellite System) water vapor chromatography and a water conservancy rain-measuring radar. The rainfall inversion method comprises the following steps: collecting GNSS data, water conservancy rain-measuring radar data, meteorological data and geographic information; estimating the atmospheric water vapor content SWV on the GNSS oblique signal path; performing three-dimensional water vapor chromatography to obtain a grid water vapor density value; identifying an inversion factor; and training the neural network and carrying out rainfall inversion. According to the rainfall inversion method established by the invention, GNSS water vapor chromatography and water conservancy rain measurement radar data are effectively utilized, the correlation between space-time gaseous water and liquid water is comprehensively considered, the rainfall inversion process is optimized, and a new thought is provided for refined inversion of rainfall intensity and spatial distribution.

The invention discloses a runoff prediction method for optimizing a neural network based on a hybrid optimization algorithm, and belongs to the technical field of hydrological forecasting. Neural network parameters are optimized through an SAPSO algorithm (simulated annealing particle swarm optimization), and daily runoff prediction is further carried out. The method comprises the following steps: setting parameters of a neural network and an optimization algorithm, including the number of input layer nodes, the number of hidden layer nodes, the number of training times, the population scale and the like; the method comprises the following steps: dividing ten-year daily runoff actual measurement data of a Taojiang station into a training set and a test set, and carrying out normalization processing to prepare for training and testing of a neural network; the weight and the threshold of the neural network are randomly initialized, parameters are transmitted to a simulated annealing particle swarm optimization algorithm for iterative optimization, optimal network parameters are obtained, and a prediction network model is established; and training the network model by using the grouped training set data, and predicting the runoff volume of the Taojiang station by using the training model. And the prediction precision of the runoff volume is obviously improved.

The invention relates to the technical field of meteorological monitoring and forecasting, and discloses a Beidou / GNSS water vapor-based rainfall forecasting method and device, and the method comprises the steps: S1, laying a Beidou / GNSS monitoring station and a meteorological station; s2, receiving observation data from a Beidou / GNSS monitoring station, and synchronously collecting meteorological data of a meteorological station; the observation data comprises satellite observation values of pseudo range, carrier phase, Doppler frequency shift and signal intensity of a Beidou / GNSS satellite and ephemeris data, and the meteorological data comprises temperature, air pressure, humidity, wind speed, wind direction and precipitation; s3, calculating observation data by using a precise point positioning algorithm, and calculating troposphere atmospheric delay and the like; according to the method and the device, high-precision and real-time rainfall forecasting is realized based on Beidou / GNSS water vapor data and an artificial intelligence analysis model, and the timeliness and the accuracy of early warning are remarkably improved. By monitoring the dynamic change of the atmospheric precipitable water amount (PWV) in real time, the water vapor accumulation trend can be sensed in advance, and efficient early warning is provided for a rainfall event.

The invention provides a GNSS-based mountain area highway loess slope stability evaluation method and system, and relates to the technical field of loess slope stability evaluation.The method comprises the steps that firstly, a slope area is divided into grids, geologic feature data of each grid are obtained, super nodes are divided according to the geologic feature data, and the super nodes are divided into super nodes; monitoring points are determined, and the soil moisture content is monitored, so that the moisture content of the soil at different depths is analyzed; secondly, the original displacement is monitored through a GNSS, displacement correction is carried out based on the Fredld unsaturated soil shear strength theory, and the actual displacement is obtained; the influence of the accumulated rainfall on the soil body strength parameters is analyzed, and the soil body strength parameters are corrected in real time; coupling the corrected soil body strength parameter with the actual displacement, calculating a safety coefficient, and judging an early warning state according to the rainfall sensitivity coefficient; and finally, comprehensively judging the stability of the slope according to the early warning state. According to the method, through multi-parameter comprehensive analysis, the accuracy of slope stability evaluation is improved.

The invention provides a runoff forecasting method and device based on soil humidity calibration, and relates to the technical field of forecasting. The method comprises the steps of generating weather forced data of a WRF mode based on an atmosphere reanalysis data set under the condition that a target area has no runoff observation data; acquiring an observed value of surface soil humidity of a target area in the external meteorological data set; inputting the meteorological forced data into a distributed hydrological model WRF-Hydro model coupled with the WRF mode, and performing simulation to obtain a simulation value of the surface soil humidity of the target area; calibrating a key parameter set of the hydrological model according to the difference between the observed value and the simulated value of the soil humidity and a historical flood event of the target area to obtain a general parameter set of the target area; and using the general parameter set to drive a WRF-Hydro model to carry out runoff forecasting on the target area. Based on the scheme, runoff forecasting can be accurately carried out on the area without runoff observation data.

This invention discloses a method for optimizing the layout of a rain gauge network in mountainous areas based on radar rainfall data, which includes Step 1: collecting rainfall data and dividing the target area into meshes, Step 2: identifying abnormal rain gauge station locations, Step 3: determining candidate locations for additional rain gauge stations, Step 4: evaluating the accuracy and effectiveness of different rain gauge network optimization methods, and Step 5: determining the optimal rain gauge network layout method. [Effects] The method of the present invention, combined with radar rainfall data, realizes effective planning and optimization of rainfall observation networks in complex mountainous terrain conditions, further improves the accuracy and reliability of precipitation observation in mountainous watersheds, and provides more accurate data support for the management and prevention of mountainous flood disasters.

The invention discloses an approaching rainfall forecasting method based on radar extrapolation of an evolutionary network and a Unet network and a Z-R relation. The method comprises the following steps: acquiring historical live rainfall data in an hourly period and using the historical live rainfall data to construct a radar echo sample data set; constructing a radar echo short-time proximity extrapolation forecasting model, wherein the radar echo short-time proximity extrapolation forecasting model comprises an evolution network module and a Unet variant module; constructing a model training loss function, and training a radar echo short-time proximity extrapolation forecasting model in combination with the radar echo sample data set; and obtaining radar jigsaw echoes one hour before the prediction time, inputting the radar jigsaw echoes into the trained model to obtain predicted radar echoes, and obtaining a rainfall grid point prediction field based on the Z-R relational expression and the predicted radar echoes. By designing the evolutionary network module, the Unet variant module and the loss function for the generator and the discriminator, the forecasting precision of the close heavy rainfall is improved, the method has a good application prospect, and a more accurate forecasting service is provided for disaster prevention and reduction.

The utility model provides a riverway water regimen monitoring device, relates to the technical field of monitoring devices, and aims to solve the problems that in a severe weather environment, a cross rod of the riverway water regimen monitoring device extends too much, so that the contact surface with wind is easily increased, and torsion and even fracture phenomena are easily caused under the action of wind power. A monitoring part is arranged outside the mounting part; and a contraction part is arranged in the mounting part. A bevel gear is arranged on the outer wall of a torsion cross rod, connection with a control motor in an assembling groove is facilitated, the torsion cross rod is controlled to rotate under the action of the control motor, and a telescopic cross rod is controlled to be adjusted through a threaded groove in the inner wall in the rotating process; and the relative length of the contraction part can be conveniently shortened by contracting the telescopic cross rod in strong wind weather, so that the contact area with wind is reduced, and the probability of bending and breaking phenomena is reduced.

The invention discloses a high-resolution peak accumulated snow depth inversion device and method based on a geographically weighted random forest, and the device comprises a data obtaining module which is used for obtaining the multi-source data of a research region, and the multi-source data comprise MODIS remote sensing accumulated snow data, Sentinel-1VV / VH backscattering data, topographic data, and meteorological station snow depth observation data; the high-resolution snow coverage data set construction module is used for processing the MODIS remote sensing snow data to obtain a high-resolution cloudless binary snow coverage data set; the feature construction module is used for constructing a comprehensive feature set containing a snow space-time process feature index, a back scattering feature index and a topographic feature index; and the model construction and analysis module is used for training a geographically weighted random forest model through the comprehensive feature set, and inverting the peak snow depth of the research area according to the optimal model. According to the invention, estimation of high-resolution peak snow depth can be realized.

A vehicle control method is provided for a vehicle having an ultra wide band (UWB) sensor configured to measure a rainfall value. The method includes waking up the UWB sensor in response to a trigger instruction for starting rainfall detection, to cause the UWB sensor to measure a rainfall value; acquiring the rainfall value measured by the UWB sensor; and controlling the vehicle to execute a preset control instruction action according to the rainfall value.

The disclosure relates generally to methods and systems for ground water prediction using integration of satellite and auxiliary observations. Conventional techniques in the art for the ground water level prediction are not accurate and efficient for predicting the ground water level. The present disclosure combines time-series archived remote sensing data with a wide array of past auxiliary datasets specific to a given region for precise prediction of future ground water condition, to predict the ground water situation i.e. rising or declining for a given region. These encompass diverse information such as weather conditions, soil properties, agricultural data, population statistics, water resources information, industrial details, drought records, seismic data, and surface deformation patterns. The present disclosure also considers different future scenarios which can be built on future cropping patterns, forecasted weather conditions, use of irrigation system in future, and future population growth of the given region.