Precipitation concentration index management by adaptive neuro-fuzzy methodology

Petković, Dalibor; Gocić, Milan; Trajković, Slaviša; Milovančević, Miloš; Šević, Dragoljub

doi:10.1007/s10584-017-1907-2

Cited by 46 publications

(5 citation statements)

References 58 publications

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“…The rainfall concentration is a key element of the Mediterranean intra-annual climate variability. An unbalanced distribution of rainfall evokes periods of rainfall excess or drought which make plant and crop growth difficult [50][51][52][53]. Thus, daily rainfall analysis is a subject of great interest in Morocco because of the high amounts of annual total precipitation falling in a limited number of very rainy days.…”

Section: Methodsmentioning

confidence: 99%

Rainfall distribution and trends of the daily precipitation concentration index in northern Morocco: a need for an adaptive environmental policy

et al. 2019

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A national priority at the Moroccan level is to understand the spatiotemporal rainfall irregularity to anticipate suitable strategies for water and agriculture management. The most northern mountainous region shows intense sensitivity to extreme hydroclimatic hazards which may compromise the future of socioeconomic acceleration and further weaken environmental balances. However, planning and decision making require accurate data, on a detailed scale and with good spatial distribution which is not the case in several south-Mediterranean countries where rainfall data and/or wellestablished hydro-meteorological network is lacking. In this paper, abundant rain gauge measurements combined with time-series open data (1958-2015) were used to study the rainfall variability. Results show an annual rainfall increase trend since 1996 with frequent, intense and abundant rains causing violent torrents and floods. This humid trend alternates with short but severe dry periods. Spatial distribution shows a rainfall increase from eastern to western area and from the coastal plains to high mountains. Based on a continuous daily rainfall dataset (from 1988 to 2012) from six scattered stations, it appears that the pattern of heavy and extreme wet days takes the form of an irregular interannual cycle. Trends of the annual precipitation daily concentration indicate a significant positive trend in the center than in the East of the study area. Considering these observations, a review of water needs/obligations priorities seems crucial so that a new conscious and adaptative environmental policy would approve new procedures for a sustainable management.

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Section: Methodsmentioning

confidence: 99%

Rainfall distribution and trends of the daily precipitation concentration index in northern Morocco: a need for an adaptive environmental policy

et al. 2019

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“…ANFIS model is based on data pairs from the system, and they combine the advantages of fuzzy logic FL and artificial neural networks ANN. ANFIS has been utilized in a variety of applications, and it is becoming increasingly popular in many research fields [14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Mohamed,

Atyia

2024

Phys. Scr.

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The current research investigates the conductivity σ AC of glassy As4Ge24Te72 thin films through a combined experimental and theoretical approach. It sheds light on the complex relationship between σ AC , frequency, temperature, and film thickness. Analysis reveals that σ AC follows ω r law and exhibits hopping behavior. To effectively model and predict σ AC , an adaptive neuro-fuzzy inference system (ANFIS) is utilized. The ANFIS model successfully simulates the experimental data, showing high accuracy. Additionally, the prediction of experimentally measured values of σ AC is processed as a testing step and provides acceptable results. That enables ANFIS to enlarge the scale and complete the missing parts in the trained datasets by predictions for unmeasured σ AC values. The ANFIS network was built using MATLAB-R2017a. It consists of two inputs (frequency and temperature) and one output (AC conductivity). The precision is confirmed by calculating the modeling errors across the training process. The mean absolute errors MAE through the modeling process are not exceeding 10 − 5 . This average error value demonstrates the model’s effective ability. The alignment between experimental and ANFIS modeling results open up exciting possibilities for future research. By predicting the properties of the understudy material using ANFIS model, we can pave the way for more compact and efficient electrical devices.

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“…These changes greatly influence precipitation patterns. Persistent extreme precipitation and drought are among the most devastating extreme weather and climate events in China and worldwide (Zhang et al ., 2009; Petković et al ., 2017; Huang et al ., 2019). Climate models predict that global warming will become more serious in the future, altering global and regional precipitation patterns on both time and spatial scales, significantly decreasing annual precipitation.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variation in precipitation concentration and its potential relationship with drought under different scenarios in Inner Mongolia, China

Guo

Zhang

et al. 2022

Intl Journal of Climatology

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Global warming has altered the uniformity of precipitation in Inner Mongolia, China, eventually leading to droughts. Further studies are necessary to determine the relationship between the concentration of precipitation and drought.Therefore, we assessed the spatial and temporal characteristics of the precipitation concentration degree (PCD), precipitation concentration period (PCP), and standardized precipitation evapotranspiration index (SPEI) in Inner Mongolia in the past and predict changes in the three indices under different scenarios in the future (2018-2100) using measured model data and Sen's slope and Mann-Kendall trend analysis. The correlation between PCD/PCP and SPEI was explored using Pearson's correlation coefficient. The results showed that the spatial distribution of PCD and PCP in Inner Mongolia exhibited significant east-west differences. The PCD values were 0.42-0.76, with high-value areas in the east. PCD showed a decreasing trend in both historical and future scenarios, indicating an even distribution of precipitation and an increased risk of drought. The PCP values were 190 -226 , with high-value areas mainly in the western region. Except for in the Representative Concentration Pathways RCP4.5 and RCP8.5, PCP values in the historical and RCP2.6 scenarios showed a decreasing trend, indicating an earlier onset of maximum precipitation. SPEI values ranged between −1.23 and 1.17, with all future scenarios showing a decreasing trend and the historical scenario showing an increasing trend. The stations with positive correlation between SPEI and PCD accounted for 89.13, 67.39, 91.3, and 95.65% of Inner Mongolia, while those with positive correlation with PCP accounted for 43.47, 60.87, 56.52, and 4.35%, indicating that the correlation between drought variation and precipitation concentration is strong. These results can help reduce and prevent droughts and floods caused by changes in precipitation patterns and provide a basis for the rational use of water resources for preventing droughts and making relief decisions.

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Precipitation concentration index management by adaptive neuro-fuzzy methodology

Cited by 46 publications

References 58 publications

Rainfall distribution and trends of the daily precipitation concentration index in northern Morocco: a need for an adaptive environmental policy

Rainfall distribution and trends of the daily precipitation concentration index in northern Morocco: a need for an adaptive environmental policy

Predicting the AC conductivity of semiconductor composition thin films using ANFIS model; an integrated experimental and theoretical approach

Spatiotemporal variation in precipitation concentration and its potential relationship with drought under different scenarios in Inner Mongolia, China

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