Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 398 - 398

Published: March 30, 2025

Solar radiation is one of the most abundant energy sources in world and a crucial parameter that must be researched developed for sustainable projects future generations. This study evaluates performance different machine learning methods solar prediction Konya, Turkey, region with high potential. The analysis based on hydro-meteorological data collected from NASA/POWER, covering period 1 January 1984 to 31 December 2022. compares Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), Artificial Networks (MLANN, RBANN). variables used include temperature, relative humidity, precipitation, wind speed, while target variable radiation. dataset was divided into 75% training 25% testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2). results indicate Bi-LSTM models performed best test phase, demonstrating superiority deep learning-based approaches prediction.

Language: Английский

Impact of Drought on Farmers’ Livelihood Vulnerability: A Case Study of County-level Units in Western Jilin Province, China

Chinese Geographical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

Drought Driving Factors as Revealed by Geographic Detector Model and Random Forest in Yunnan, China

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 505 - 505

Published: March 12, 2025

Yunnan Province, as a critical ecological security barrier in China, has long been highly susceptible to drought events. Characterizing the spatiotemporal distributions of and identifying its driving factors is crucial. Due complexity occurrence, linear correlation analysis alone insufficient quantify drivers their interactions. This study used Standardized Precipitation Evapotranspiration Index (SPEI) indicator analyze trends across six major river basins. The geographic detector model (GDM) random forest (RF) were utilized impacts meteorological, topographical, soil, human activities on drought, well interactions among these factors. results showed that 63.61% area exhibits significant drying trend (p-value < 0.05), with Jinsha River Basin (JSRB) experiencing highest frequency extreme (PRE), temperature, potential evapotranspiration (PET), vapor pressure deficit (VPD), relative humidity (RH) identified primary controlling factor displaying nonlinear enhancement effects. PRE plays dominant role Yunnan, whereas elevation primarily influenced severity JSRB, Lancang (LCRB), Nujiang (NJRB). RF-based SPEI prediction demonstrated superior performance simulating short-term (SPEI_1, R2 > 0.931, RMSE 0.279), particularly JSRB (R2 = 0.947 0.228). These findings provide scientific basis for regional water resource management applications early warning systems, offering robust framework understanding mitigating ecologically sensitive regions.

Language: Английский