Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences.

Objective: This study delves into the impact of urban meteorological elements-specifically, air temperature, relative humidity, and atmospheric pressure-on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius), air pressure (in hectopascals), and relative humidity (in percent) were used for the statistical period 2017-2023. Various models, including the correlation coefficient, generalized additive models (GAM), generalized linear models (GLM), and support vector machines (SVM), were employed to scrutinize the data.

The effect of climatic and geographical factors on breast cancer in Iran.

Objective: By studying the effect of environmental factors on health, it is clear that geographical, climatic and environmental factors have a significant impact on human health. This study, based on the data of the patients with breast cancer in Iran since 2010 to 2014 and using the statistical methods has determined the effect of geographical features of Iran (solar radiation status, radiation angle) on the frequency and distribution of this disease.

Results: The maximum amount of total solar radiation occurs in the vicinity (surrounding) of the tropic of cancer, which covers some parts of the south of Iran and in the atmosphere of the northern latitudes of Iran.

Investigating climatic changes of the wind regime over Western Iran.

Objective: The aim of the present study was to reveal changes in the wind regime by investigating wind-speed data from meteorological stations in western Iran and comparing them in the last three decades (1986-2015).

Results: Two main groups of daily cycles were identified; one group with a single peak and one group with two or more peaks. Using spectral decomposition technique, it was revealed that the heterogeneity observed in the area in terms of altitude and topography results in differences in the density of the spectra with similar frequencies.

A comparison of three data mining time series models in prediction of monthly brucellosis surveillance data.

The early and accurately detection of brucellosis incidence change is of great importance for implementing brucellosis prevention strategic health planning. The present study investigated and compared the performance of the three data mining techniques, random forest (RF), support vector machine (SVM) and multivariate adaptive regression splines (MARSs), in time series modelling and predicting of monthly brucellosis data from 2005 (March/April) to 2017 (February/March) extracted from a national public health surveillance system in Hamadan located in west of Iran. The performances were compared based on the root mean square errors, mean absolute errors, determination coefficient (R ) and intraclass correlation coefficient criteria.

Investigating the effect of climatic parameters on mental disorder admissions.

The main objective of this study was to evaluate the role of climatic parameters and phenomena including the monthly number of dusty/rainy/snowy/foggy days, cloudiness (Okta), horizontal visibility, and barometric pressure (millibar) on major depressive disorder, bipolar, schizophrenia, and schizoaffective admissions. The monthly data related to the number of admissions in Farshchian hospital and climatic parameters from March 2005 to March 2017 were extracted. Random forest regression and dynamic negative binomial regression were used to examine the relationship between variables; the statistical significance was considered as 0.