Air temperature is an essential climatic component particularly in water resources management and other agro-hydrological/meteorological activities planning This paper examines the prediction capability of three machine learning models, least square support vector machine (LSSVM), group method and data handling neural network (GMDHNN) and classification and regression trees (CART) in air temperature forecasting using monthly temperature data of Astore and Gilgit climatic stations of Pakistan. The prediction capability of three machine learning models is evaluated using different time lags input combinations with help of root mean square error (RMSE), the mean absolute error (MAE) and coefficient of determination (R²).statistical indicators. The obtained results indicated that the LSSVM model is more accurate in temperature forecasting than GMDHNN and CART models. LSSVM significantly decreases the mean RMSE of the GMHNN and CART models by 1.47-3.12% and 20.01-25.12% for the Chakdara and Kalam Stations, respectively.

This study investigates the effects of using livestock manure (LM), livestock manure ash (LMA) and the combination of LM with LMA (LM + LMA) on the soil moisture content and water infiltration rate of clay, clay loam and sandy loam soils. The soil moisture content and water infiltration rate were 11.60, 8.90 and 6.85% and 1.98, 1.55 and 1.62 cm h‾¹ for clay, clay loam and sandy loam soils, respectively, by the application of 15 t ha‾¹ LM. Moreover, using 15 t ha‾¹ of LMA had the most desirable effects on the soil moisture content in the clay and clay loam soils. Using this treatment, the soil moisture content and water infiltration rate were 10.85, 11.20 and 7.24% and 2.40, 1.90 and 1.75 cm h‾¹ for clay, clay loam and sandy loam soils, respectively. Promising results were obtained using 15 + 15 t ha‾¹ of LM + LMA, where the soil moisture content and water infiltration rate were 11.50, 10.20 and 8.20% and 2.80, 1.90 and 1.72 cm h‾¹ for clay, clay loam and sandy loam soils, respectively. Both LMA and its combination with LM were found to be positive strategies to achieve sustainable agricultural goals because of the increased water infiltrates (less runoff) available for the plants.