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Accurate and timely information about production estimates of wheat is useful for policymakers and government planners. The traditional methods for yield forecasting are labor insensitive and time-consuming therefore remote sensing is an effective approach for precise yield forecasting. The study was planned to develop a comprehensive framework for yield forecasting and to assess interannual yield variability in semi-arid regions. For wheat area classification, the peak season Landsat-8 satellite images were acquired, and Top of Atmospheric (TOA) correction was performed. The ground-truthing points of 100 farms were collected from the study area for the training of algorithms. The eight machine learning algorithms were used tune and tested using 10-k fold cross-validation and the best model was used for land cover classification of wheat. For yield forecasting, the temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) were derived for the wheatgrowing season from November to April. A Principal Component Analysis (PCA) was used to variable selection and then Least Absolute Shrinkage Selection Operator (LASSO) analysis was performed to develop coefficients of the yield forecasting model. The developed model was further used in yield forecasting of 10 years (2008-2018) in four semi-arid regions. The predicted yield was compared with Crop Reporting Service (CRS), Pakistan department. The results of all machine learning algorithms showed an accuracy of 88% to 96%, however, the Random forest algorithm showed higher accuracy, which was further used for classification. The wheat estimated area of 6.9% was less than reported by CRS. For interannual variability, the relationship of observed (CRS) and predicted yield of 10 years showed a close relation with R2 ranged from 0.69 to 0.75 in the semi-arid region of Punjab, Pakistan. It was concluded that machine learning algorithms can be used as novel tools for yield forecasting and assessment of interannual yield variability. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95–99%; r = 0.96–1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models. [ABSTRACT FROM AUTHOR]

Multi-Model Ensembles (MMEs) are often employed to reduce the uncertainties related to GCM simulations/projections. The objective of this study was to evaluate the performance of MMEs developed using machine learning (ML) algorithms with different combinations of GCMs ranked based on their performance and determine the optimum number of GCMs to be included in an MME. In this study ML algorithms; Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Relevance Vector Machine (RVM) were used to develop MMEs for annual, monsoon and winter; precipitation (P), maximum (T max) and minimum (T min) temperature over Pakistan using 36 Coupled Model Intercomparison Project Phase 5 GCMs. GCMs were ranked using Taylor Skill Score for individual seasons and variables, and then using a comprehensive Rating Metric (RM) overall rank of each GCM was determined. It was found that, HadGEM2-AO is the most skilled GCM and IPSL-CM5B-LR is the least skilled GCMs in simulating the 3 climate variables. The performance of MMEs did not improve after the inclusion of about 18 top-ranked GCMs. Thus, it was understood that the optimum performance of MMEs is achieved when about 50% of the top-ranked GCMs are used. The inter-comparison of MMEs developed with ANN, KNN, SVM and RVM revealed that KNN and RVM-based MMEs show better skills. It was found that RVM yields MMEs which show smaller variations in performance over space unlike ANN which displayed large fluctuations in performance over space. KNN and RVM are recommended over SVM and ANN for the development of MMEs over Pakistan. • Optimum performance of multi-model ensemble is achieved with 50% of top-ranked GCMs. • K -Nearest Neighbour and Relevance Vector Machine are good for multi-model ensembles. • Artificial Neural Network multi-model ensembles showed large performance fluctuations in space. • Machine learning-based multi-model ensembles outperformed simple ensmeble mean. [ABSTRACT FROM AUTHOR]