Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin,Western Himalaya:Using Markov Chain and LSTM approaches

This research aim to evaluate hydro-meteorological data from the Yamuna River Basin,Uttarakhand,India,utilizing Extreme Value Distribution of Frequency Analysis and the Markov Chain Approach.This method assesses persistence and allows for combinatorial probability estimations such as initial and transitional probabilities.The hydrologic data was generated(in-situ)and received from Uttarakhand Jal Vidut Nigam Limited(UJVNL),and meteorological data was acquired from NASA’s archives MERRA-2 product.A total of sixteen years(2005-2020)of data was used to foresee daily Precipitation from 2020 to 2022.MERRA-2 products are utilized as observed and forecast values for daily Precipitation throughout the monsoon season,which runs from July to September.Markov Chain and Long Short-Term Memory(LSTM)findings for 2020,2021,and 2022 were observed,and anticipated values for daily rainfall during the monsoon season between July and September.According to test findings,the artificial intelligence technique cannot anticipate future regional meteorological formations;the correlation coefficient R^(2) is around 0.12.According to the randomly verified precipitation data findings,the Markov Chain model has a success rate of 79.17 percent.The results suggest that extended return periods should be a warning sign for drought and flood risk in the Himalayan region.This study gives a better knowledge of the water budget,climate change variability,and impact of global warming,ultimately leading to improved water resource management and better emergency planning to the establishment of the Early Warning System(EWS)for extreme occurrences such as cloudbursts,flash floods,landslides hazards in the complex Himalayan region.

Preface to the Special Issue on the"Forecast and Evaluation of Meteorological Disasters"(FEMD)

Meteorological disasters usually exert huge impacts on the development of both human society and the economy. According to statistics from the United Nations International Strategy for Disaster Reduction, the annual mean economic loss caused by meteorological disasters accounts for 3%-6% of the total amount of global GDP. China is a country that has been one of the most severely influenced by natural disasters.

Early Warning of Snow Disaster in Pasturing Areas of Inner Mongolia

Using ground and remote sensing monitoring, and national standards of snow disaster monitoring evaluation standards, quantitative evaluation of snow disaster was realized. Threshold of animal husbandry weather forecast indexes were applied to establish snow disaster early warning for different grasslands in pasturing areas of Inner Mongolia, and make the grade distribution map of snow disaster early warning. The forecast results basically met the real conditions, it proved that this forecast method was capable of evaluating scale and influence degree of snow disaster. However, the snow disaster grade forecast deviated from the real conditions for the influence of weather forecast accuracy rate.

The Application of a Grey Markov Model to Forecasting Annual Maximum Water Levels at Hydrological Stations

Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.

Advanced prediction for multiple disaster sources of laneway under complicated geological conditions

The driving safety in the laneway is often controlled by multiple disaster sources which include fault fracture zone, water-bearing body, goaf and collapse column. The advanced prediction of them has become a hotspot. Based on analysis of physical characteristics of the disaster sources and comparative evaluation of accuracy of the main advanced geophysical detection methods, we proposed a comprehen- sive judging criterion that tectonic interface can be judged by the elastic wave energy anomaly, strata water abundance can be discriminated by apparent resistivity response difference and establish a reason- able advanced prediction system. The results show that the concealed disaster sources are detected effec- tively with the accuracy rate of 80% if we use advanced prediction methods of integrated geophysics combined with correction of seismic and electromagnetic parameters, moreover, applying geological data, we may then distinguish types of the disaster sources and fulfill the qualitative forecast. Therefore, the advanced prediction system pays an important referential and instructive role in laneway driving project.

A brief introduction to disaster rock mass mechanics

Rock mass mechanics can be classified into engineering rock mass mechanics and disaster rock mass mechanics based on science and application.Their conception,object,scientific essence and application were elaborated.The connotation,studying method and theoretical framework of disaster rock mass mechanics were described.Disaster rock mass mechanics is a strongly nonlinear discipline which is a strong tool to study natural and artificially-induced disasters.The rock mass system where disasters happen exhibits extremely spatial-temporal nonlinearity in the critically unstable state.Hence,the potentially effective prediction and forecasting of disasters depends on statistical analysis of highly probable events.The direction of efforts for predicting and forecasting disasters could be to find the quantitative or semi-quantitative relationship between physical and biological information and instability of rock mass system.