Browsing by Autor "Majid Javari"

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A hybrid machine learning approach to analyzing the impacts of urban development on land surface temperature and the urban heat island effect in Isfahan
(Springer Science+Business Media, 2025) Majid Javari
Assessment of dynamic linear and non-linear models on rainfall variations predicting of Iran
(2017) Majid Javari
The main research aims to detecting the linear and nonlinear variability modeling in analyzing the variability patterns of rainfall series. For rainfall linear and nonlinear variability modeling, the ARIMA models and ARCH family models has been used for predicting the monthly and annual rainfall series extracted from IRIMO during 1975-2014 within 140 stations in Iran. Several ARIMA and ARCH (six models) models have been used and their validity has been confirmed by evaluating different accuracy indicators, using the hybrid model for the variability modeling. The analysis of ARIMA and GARCH selective models indicates existence of random and non-random in the rainfall time series. The combination model of (1, 0, 0) and GARCH (1, 1) is applied for the estimate and prediction of monthly rainfall. With careful valuation of the hybrid model, the ARIMA (1,0,0) and GARCH(1,1) is recognized as the significant acceptable model by determines of different accuracy indicators similar to mean squared error (77025.34); root mean squared error (277.53); mean absolute error (167.68); mean absolute percentage error (79.68); and Theil’s U coefficient (0.365). However, the results showed that hybrid model, as a variability model is more efficient in forecasting the rainfall variability and underlying this model can be used as a variability forecast model and chaos phenomena in Iran. In addition, a nonlinear model (ARCH family, especially GARCH1, 1) provides a quantitative-analytical method to distinguish between a particular random and non-random model for rainfall variability in Iran. Keywords : Linear models, Non-linear models, variability severity and rainfall variability.
Measuring trends and regimes of rainfall with the use of seasonality patterns in Iran
(Research Square (United States), 2023) Majid Javari
Abstract The trend and regimes of rainfall considerably are different effects on the bio-environmental process. Therefore; climatic elements changes and changed trends and regimes of rainfall are both makers, with severe changes to bio-environmental conditions. In a more detailed analysis, it is essential to detect both trends and regimes of rainfall, distribution of monthly and annual rainfall, as well as seasonality patterns. Therefore, this study used monthly and annual rainfall series records for 1975–2019 from 140 synoptic stations and satellite data such as geopotential height, Southern Oscillation Index ( SOI), Northern Oscillation Index (NOI), North Atlantic Oscillation (NAO) to detect the trend and regimes of rainfall in Iran. Statistical analysis with parametric and non-parametric tests for monthly and annual rainfall series was used to detect the rainfall patterns based on selecting the Mann–Kendall test (MK), Sen.’s slope method (SSM), and the t-student test at a 5% significance level. Based on the seasonality index (SI) method to detect the seasonality patterns, we recognized spatial patterns of the regimes of rainfall and trends of rainfall in Iran. Finally, we evaluated the trends and the regimes' patterns of rainfall spatially, the type of trends with decreasing and increasing patterns, and rainfall changes range with spatial statistics models and the temporal distribution. Results show the different slopes from -0.409 to -0.156 mm based on spatial statistics models with the south-north oriented with a total decreasing pattern for rainfall changes. Rainfall patterns show the different patterns temporally (non-trend, decreasing, and increasing) based on stations with different periods in Iran, which may relate this difference to temporal distribution in recorded periods. Based on the seasonality, rainfall regime distribution shows diversity in seasonality in rainfall from an extreme seasonality pattern, mainly in most rainfall in < 3 months pattern, markedly seasonal with a long dry season pattern, mainly seasonal pattern, and extreme seasonality pattern in Iran. Therefore, the results of this study, effects of trends, and rainfall regimes were shown on environmental planning in Iran.
Multifactorial Impacts of Climatic Variables and Extreme Indices on Human Thermal Comfort in Iran
(2025) Majid Javari
Climatic comfort, a pivotal element in elevating human well-being and health, is influenced by an intricate network of meteorological variables. This investigation employs a structural equation modeling (SEM) approach, specifically partial least squares, alongside spatial modeling, and machine learning to scrutinize the multi-faceted impact of various cli-matic constructs on composite indices of climatic comfort across Iran. The constructs under examination encompass thermal, radiative, humidity, wind, and pressure elements, as well as distinct extreme climatic phenomena. Thermal constructs such as diurnal and annual temperature ranges, dew point temperature, and annual minimum and maxi-mum temperatures are analyzed within the model, alongside radiation constructs, including shortwave radiation and albedo. Similarly, humidity and wind constructs, represented by variables like relative humidity, precipitation, and wind speed and direction, are incorporated. Furthermore, specific climatic events, such as summer days, warm nights, and the Warm Spell Duration Index (WSDI), enhance understanding of the climatic conditions shaping thermal comfort. Composite comfort indices, including the Predicted Mean Vote (PMV), Universal Thermal Climate Index (UTCI), and Wet Bulb Globe Temperature (WBGT), serve as the ultimate criteria for comfort assessment in the model. Modeling outcomes reveal that thermal conditions (with a coefficient of 0.515) and extreme climatic occurrences (with a coefficient of 0.381), notably summer days, warm nights, and heat spell duration, exert the most pronounced positive and direct influence on the composite climatic comfort indices. These findings corroborate the primary role of elevated temperatures and extreme events in engendering thermal discomfort within Iran, particularly in southern and southeastern regions (e.g., the Per-sian Gulf and Oman Sea coasts), which experience the highest intensity of these phenomena. Conversely, solar radiation demonstrated a moderate inverse effect (-0.298), while humidity conditions (-0.074) and wind/pressure (0.043) exhibited weaker impacts on comfort. The model's substantial explanatory power (R2=0.811) and robust predictive capability (Q2=0.620) underscore its high efficacy in elucidating and forecasting climatic comfort variations. Moreover, the com-plete alignment of spatial patterns derived from comfort index maps (PMV, UTCI, WBGT, Humidex, TDI) and extreme event maps (Summer Days, Warm Nights, WSDI) with the model's results validates the accuracy and credibility of the findings in pinpointing areas with significant thermal challenges (south and southwest) and regions with more favora-ble comfort levels (mountainous and northern areas). Consequently, the interplay of these constructs and their reciprocal effects plays a crucial role in shaping climatic comfort conditions across diverse Iranian locales. This research can offer substantial guidance for policymakers in devising mechanisms to enhance climatic quality and manage urban and re-gional climatic change.
Simulation of precipitation variations in Iran using Kriging interpolation methods
(Academic Journals, 2017) Majid Javari
This study estimated spatial variability of precipitation in the monthly and annual scales in Iran for the period of 1975 to 2014 in 140 stations using kriging interpolation methods. In precipitation variability analysis three procedures were used: Mann-Kendall test, Sen's slope estimator and spatial trend patterns. Results show that there are both increasing and decreasing trends in monthly precipitation in Iran. Based on the magnitude of the significant trend, there are three patterns of the significant trend (average, lower and upper) in the monthly precipitation of Iran that vary from -0.0785 mm/month in October to 0.1536 mm/month in November. As a result, in January, February, March, May, October, and December, the magnitude of negative trends and in April and November the random and positive patterns were estimated in the precipitation in 140 stations, respectively. Key words: Spatial variations, trend variations, spatial variability, Mann-Kendall, precipitation.