Population Forecasting and Climate–Demography Association in Soran, Iraq: An Exploratory Time-Series Analysis

Rapid urban population expansion in semi-arid environments poses considerable challenges for sustainable planning, particularly where demographic growth occurs under environmental constraints. This study examines the statistical association between climatic variability and population change in the Soran district center, Kurdistan Region of Iraq, and develops an exploratory framework for forecasting future demographic trends. It uses the official annual population series compiled by the Statistics Directorate of the Soran Independent Administration for 2010–2024 (15 observations) together with monthly meteorological records for 2015–2024 from the Soran agro-meteorological station. Two properties of the demographic record govern what can be inferred from it. The 2010–2023 values are inter-censal estimates reproduced to within 0.10% by a single declining-growth rule, so goodness-of-fit statistics obtained on that segment measure agreement with an interpolation rule rather than forecasting skill; and the 2024 value derives from the 2024 census round, representing a level shift of +18.4% against a 2.0% per year trend—178 residual standard deviations—rather than a year of growth. Detrended, lagged Spearman correlations across twelve climate–lag combinations yielded no association surviving Holm–Bonferroni correction; because the pre-2024 values are administratively smoothed, this is reported as an inability of the available data to resolve a climate–demography association rather than as evidence of independence. Eight specifications—naïve, drift and linear-trend benchmarks, a second-order polynomial, a log-linear trend, an AIC-selected ARIMA, and two multilayer perceptrons—were compared under identical rolling-origin cross-validation, producing seven one-step-ahead forecasts each. On the inter-censal segment a random walk with drift attained a mean absolute percentage error of 0.087%, better than every fitted specification; on the census fold every specification erred by at least 11,663 persons. The reported 2025–2030 projection is therefore anchored on the 2024 census level with growth extrapolated from its 2011–2023 trend, giving 93,342 residents in 2025 and 101,691 in 2030 (scenario range 100,395–104,064), reproduced to within 488 persons by an ARIMA(0,2,2) with a 2024 step term. Polynomial and log-linear trends fitted across the discontinuity project 2025 values are below the observed 2024 population and are reported as specification failures. The study demonstrates both the potential and the limitations of demographic forecasting in data-scarce semi-arid urban settings and shows that the provenance of an administrative population series materially constrains the claims such a study can make.