• Energy Research

Assessment of possible changes in crops water stress due to climate alteration is essential for agricultural planning, particularly in arid regions where water supply is the major challenge for agricultural development. This study aims to project climatic water availability (CWA) and crop water demand (CWD) to outline the possible future agricultural water stress of Iraq for different radiative concentration pathways (RCPs). The ensemble means of downscaled precipitation and temperature projections of the selected global climate models (GCMs) were used in a simple water balance model for this purpose. The modified Mann–Kendall (mMK) trend test was employed to estimate the tendency in CWA and the Wilcoxon rank test to evaluate CWD alteration in three future time horizons compared to the base period (1971–2000). The results revealed a decrease in CWA at a rate of up to −34/year during 2010–2099 for RCP8.5. The largest declination would be in summer (−29/year) and an insignificant decrease in winter (−1.3/year). The study also showed an increase in CWD of all major crops for all scenarios. The highest increase in CWD would be for summer crops, approximately 320 mm, and the lowest for winter crops, nearly 32 mm for RCP8.5 in the far future (2070–2099). The decrease in CWA and increase in CWD would cause a sharp rise in crop water stress in Iraq. This study indicates that the increase in temperature is the main reason for a large increase in CWD and increased agricultural water stress in Iraq.

L'utilisation durable du rayonnement solaire librement disponible comme source d'énergie renouvelable nécessite des modèles prédictifs précis pour évaluer quantitativement les potentiels énergétiques futurs. Dans cette recherche, une évaluation de la précision du modèle de machine d'apprentissage extrême (ELM) en tant que cadre rapide et efficace pour estimer le rayonnement solaire incident global (G) est entreprise. Des ensembles de données météorologiques quotidiennes adaptés à l'estimation de G appartiennent aux parties nord du bassin de Cheliff, dans le nord-ouest de l'Algérie, et sont utilisés pour construire le modèle d'estimation. Des fonctions de corrélation croisée sont appliquées entre les entrées et la variable cible (c'est-à-dire G) où plusieurs informations climatologiques sont utilisées comme prédicteurs pour l'estimation du niveau de surface G. Les entrées de modèle les plus significatives sont déterminées conformément aux corrélations croisées les plus élevées compte tenu de la covariance des prédicteurs avec l'ensemble de données G. Par la suite, sept modèles ELM avec des architectures neuronales uniques en termes de neurones d'entrée-sortie cachés sont développés avec des combinaisons d'entrée appropriées. Les performances d'estimation du modèle ELM prescrit au cours de la phase de test sont évaluées par rapport à des régressions linéaires multiples (MLR), à des modèles de moyenne mobile intégrée autorégressive (ARIMA) et à plusieurs études documentaires bien établies. Cela se fait conformément à plusieurs mesures de score statistiques. En termes quantitatifs, l'erreur quadratique moyenne (RMSE) et l'erreur absolue moyenne (MAE) sont considérablement plus faibles pour le modèle ELM optimal avec RMSE et MAE = 3,28 et 2,32 Wm -2 par rapport à 4,24 et 3,24 Wm -2 (MLR) et 8,33 et 5,37 Wm -2 (ARIMA). La utilización sostenible de la radiación solar disponible gratuitamente como fuente de energía renovable requiere modelos predictivos precisos para evaluar cuantitativamente los potenciales energéticos futuros. En esta investigación, se realiza una evaluación de la precisión del modelo de máquina de aprendizaje extremo (ELM) como un marco rápido y eficiente para estimar la radiación solar incidente global (G). Los conjuntos de datos meteorológicos diarios adecuados para la estimación de G pertenecen a las partes septentrionales de la cuenca de Cheliff en el noroeste de Argelia, se utilizan para construir el modelo de estimación. Las funciones de correlación cruzada se aplican entre las entradas y la variable objetivo (es decir, G), donde se utilizan varias informaciones climatológicas como predictores para la estimación del nivel de superficie G. Las entradas del modelo más significativas se determinan de acuerdo con las correlaciones cruzadas más altas considerando la covarianza de los predictores con el conjunto de datos G. Posteriormente, se desarrollan siete modelos ELM con arquitecturas neuronales únicas en términos de sus neuronas de entrada-salida oculta con combinaciones de entrada apropiadas. El rendimiento de estimación del modelo ELM prescrito durante la fase de prueba se evalúa frente a regresiones lineales múltiples (MLR), modelos de media móvil integrada autorregresiva (ARIMA) y varios estudios de literatura bien establecidos. Esto se hace de acuerdo con varias métricas de puntuación estadística. En términos cuantitativos, el error cuadrático medio (RMSE) y el error absoluto medio (MAE) son dramáticamente más bajos para el modelo ELM óptimo con RMSE y MAE = 3.28 y 2.32 Wm -2 en comparación con 4.24 y 3.24 Wm -2 (MLR) y 8.33 y 5.37 Wm -2 (ARIMA). Sustainable utilization of the freely available solar radiation as renewable energy source requires accurate predictive models to quantitatively evaluate future energy potentials. In this research, an evaluation of the preciseness of extreme learning machine (ELM) model as a fast and efficient framework for estimating global incident solar radiation (G) is undertaken. Daily meteorological datasets suitable for G estimation belongs to the northern parts of the Cheliff Basin in Northwest Algeria, is used to construct the estimation model. Cross-correlation functions are applied between the inputs and the target variable (i.e., G) where several climatological information's are used as the predictors for surface level G estimation. The most significant model inputs are determined in accordance with highest cross-correlations considering the covariance of the predictors with the G dataset. Subsequently, seven ELM models with unique neuronal architectures in terms of their input-hidden-output neurons are developed with appropriate input combinations. The prescribed ELM model's estimation performance over the testing phase is evaluated against multiple linear regressions (MLR), autoregressive integrated moving average (ARIMA) models and several well-established literature studies. This is done in accordance with several statistical score metrics. In quantitative terms, the root mean square error (RMSE) and mean absolute error (MAE) are dramatically lower for the optimal ELM model with RMSE and MAE = 3.28 and 2.32 Wm -2 compared to 4.24 and 3.24 Wm -2 (MLR) and 8.33 and 5.37 Wm -2 (ARIMA). يتطلب الاستخدام المستدام للإشعاع الشمسي المتاح مجانًا كمصدر للطاقة المتجددة نماذج تنبؤية دقيقة للتقييم الكمي لإمكانات الطاقة المستقبلية. في هذا البحث، يتم إجراء تقييم لدقة نموذج آلة التعلم المتطرفة (ELM) كإطار سريع وفعال لتقدير الإشعاع الشمسي الساقط العالمي (G). مجموعات بيانات الأرصاد الجوية اليومية المناسبة لتقدير G تنتمي إلى الأجزاء الشمالية من حوض Cheliff في شمال غرب الجزائر، ويستخدم لبناء نموذج التقدير. يتم تطبيق وظائف الارتباط المتبادل بين المدخلات والمتغير المستهدف (أي G) حيث يتم استخدام العديد من المعلومات المناخية كمؤشرات لتقدير المستوى السطحي G. يتم تحديد مدخلات النموذج الأكثر أهمية وفقًا لأعلى الارتباطات المتبادلة مع الأخذ في الاعتبار التباين المشترك للمتنبئين مع مجموعة البيانات G. في وقت لاحق، يتم تطوير سبعة نماذج ELM مع بنى عصبية فريدة من نوعها من حيث الخلايا العصبية المخفية للمدخلات والمخرجات مع تركيبات المدخلات المناسبة. يتم تقييم أداء تقدير نموذج علم المحدد خلال مرحلة الاختبار مقابل الانحدارات الخطية المتعددة (MLR)، ونماذج المتوسط المتحرك المتكامل الانحداري الذاتي (ARIMA) والعديد من الدراسات الأدبية الراسخة. ويتم ذلك وفقًا للعديد من مقاييس الدرجات الإحصائية. من الناحية الكمية، فإن متوسط خطأ الجذر التربيعي (RMSE) ومتوسط الخطأ المطلق (MAE) أقل بشكل كبير لنموذج ELM الأمثل مع RMSE و MAE = 3.28 و 2.32 Wm -2 مقارنة بـ 4.24 و 3.24 Wm -2 (MLR) و 8.33 و 5.37 Wm -2 (ARIMA).

An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

This study evaluates the future climate fluctuations in Iran's eight major climate regions (G1-G8). Synoptic data for the period 1995-2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020-2040), middle future (2040-2060), and far future (2060-2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson's correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995-2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by -74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by -4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (fp). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength (fm), concrete block compressive strength (fb), and height to thickness ratio (h/t). Three modeling scenarios based on the different data divisions (i.e., 80–20%, 75–25%, and 70–30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80–20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively.