Smart models to improve agrometeorological estimations and predictions

La población mundial, en continuo crecimiento, alcanzará de forma estimada los 9,7 mil millones de habitantes en el 2050. Este incremento, combinado con el aumento en los estándares de vida y la situación de emergencia climática (aumento de la temperatura, intensificación del ciclo del agua, etc.) nos enfrentan al enorme desafío de gestionar de forma sostenible los cada vez más escasos recursos disponibles. El sector agrícola tiene que afrontar retos tan importantes como la mejora en la gestión de los recursos naturales, la reducción de la degradación medioambiental o la seguridad alimentaria y nutricional. Todo ello condicionado por la escasez de agua y las condiciones de aridez: factores limitantes en la producción de cultivos. Para garantizar una producción agrícola sostenible bajo estas condiciones, es necesario que todas las decisiones que se tomen estén basadas en el conocimiento, la innovación y la digitalización de la agricultura de forma que se garantice la resiliencia de los agroecosistemas, especialmente en entornos áridos, semi-áridos y secos sub-húmedos en los que el déficit de agua es estructural. Por todo esto, el presente trabajo se centra en la mejora de la precisión de los actuales modelos agrometeorológicos, aplicando técnicas de inteligencia artificial. Estos modelos pueden proporcionar estimaciones y predicciones precisas de variables clave como la precipitación, la radiación solar y la evapotranspiración de referencia. A partir de ellas, es posible favorecer estrategias agrícolas más sostenibles, gracias a la posibilidad de reducir el consumo de agua y energía, por ejemplo. Además, se han reducido el número de mediciones requeridas como parámetros de entrada para estos modelos, haciéndolos más accesibles y aplicables en áreas rurales y países en desarrollo que no pueden permitirse el alto costo de la instalación, calibración y mantenimiento de estaciones meteorológicas automáticas completas. Este enfoque puede ayudar a proporcionar información valiosa a los técnicos, agricultores, gestores y responsables políticos de la planificación hídrica y agraria en zonas clave. Esta tesis doctoral ha desarrollado y validado nuevas metodologías basadas en inteligencia artificial que han ser vido para mejorar la precision de variables cruciales en al ámbito agrometeorológico: precipitación, radiación solar y evapotranspiración de referencia. En particular, se han modelado sistemas de predicción y rellenado de huecos de precipitación a diferentes escalas utilizando redes neuronales. También se han desarrollado modelos de estimación de radiación solar utilizando exclusivamente parámetros térmicos y validados en zonas con características climáticas similares a lugar de entrenamiento, sin necesidad de estar geográficamente en la misma región o país. Analógamente, se han desarrollado modelos de estimación y predicción de evapotranspiración de referencia a nivel local y regional utilizando también solamente datos de temperatura para todo el proceso: regionalización, entrenamiento y validación. Y finalmente, se ha creado una librería de Python de código abierto a nivel internacional (AgroML) que facilita el proceso de desarrollo y aplicación de modelos de inteligencia artificial, no solo enfocadas al sector agrometeorológico, sino también a cualquier modelo supervisado que mejore la toma de decisiones en otras áreas de interés.The world population, which is constantly growing, is estimated to reach 9.7 billion people in 2050. This increase, combined with the rise in living standards and the climate emergency situation (increase in temperature, intensification of the water cycle, etc.), presents us with the enormous challenge of managing increasingly scarce resources in a sustainable way. The agricultural sector must face important challenges such as improving natural resource management, reducing environmental degradation, and ensuring food and nutritional security. All of this is conditioned by water scarcity and aridity, limiting factors in crop production. To guarantee sustainable agricultural production under these conditions, it is necessary to based all the decision made on knowledge, innovation, and the digitization of agriculture to ensure the resilience of agroecosystems, especially in arid, semi-arid, and sub-humid dry environments where water deficit is structural. Therefore, this work focuses on improving the precision of current agrometeorological models by applying artificial intelligence techniques. These models can provide accurate estimates and predictions of key variables such as precipitation, solar radiation, and reference evapotranspiration. This way, it is possible to promote more sustainable agricultural strategies by reducing water and energy consumption, for example. In addition, the number of measurements required as input parameters for these models has been reduced, making them more accessible and applicable in rural areas and developing countries that cannot afford the high cost of installing, calibrating, and maintaining complete automatic weather stations. This approach can help provide valuable information to technicians, farmers, managers, and policy makers in key wáter and agricultural planning areas. This doctoral thesis has developed and validated new methodologies based on artificial intelligence that have been used to improve the precision of crucial variables in the agrometeorological field: precipitation, solar radiation, and reference evapotranspiration. Specifically, prediction systems and gap-filling models for precipitation at different scales have been modeled using neural networks. Models for estimating solar radiation using only thermal parameters have also been developed and validated in areas with similar climatic characteristics to the training location, without the need to be geographically in the same region or country. Similarly, models for estimating and predicting reference evapotranspiration at the local and regional level have been developed using only temperature data for the entire process: regionalization, training, and validation. Finally, an internationally open-source Python library (AgroML) has been created to facilitate the development and application of artificial intelligence models, not only focused on the agrometeorological sector but also on any supervised model that improves decision-making in other areas of interest

Comparison of predictions of daily evapotranspiration based on climate variables using different data mining and empirical methods in various climates of Iran

To accurately manage water resources, a precise prediction of reference evapotranspiration (ETref) is necessary. The best empirical equations to determine ETref are usually the temperature-based Baier and Robertson (BARO), the radiation-based Jensen and Haise (JEHA), and the mass transfer-based Penman (PENM) ones. Two machine learning (ML) models were used: least squares support vector regression (LSSVR) and ANFIS optimized using the particle swarm optimization algorithm (ANFPSO). These models were applied to the daily ETref at 100 synoptic stations for different climates of Iran. Performance of studied models was evaluated by the correlation coefficient (R), coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), scatter index (SI) and the Nash-Sutcliffe efficiency (NSE). The combination-based ML models (LSSVR4 and ANFPSO4) had the lowest error (RMSE = 0.34–2.85 mm d−1) and the best correlation (R = 0.66–0.99). The temperature-based empirical relationships had more precision than the radiation- and mass transfer-based empirical equations

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R2 = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique

AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models

Accurately forecasting reference evapotranspiration (ET0) values is crucial to improve crop irrigation scheduling, allowing anticipated planning decisions and optimized water resource management and agricultural production. In this work, a recent state-of-the-art architecture has been adapted and deployed for multivariate input time series forecasting (transformers) using past values of ET0 and temperature-based parameters (28 input configurations) to forecast daily ET0 up to a week (1 to 7 days). Additionally, it has been compared to standard machine learning models such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme learning machine (ELM), convolutional neural network (CNN), long short-term memory (LSTM), and two baselines (historical monthly mean value and a moving average of the previous seven days) in five locations with different geo-climatic characteristics in the Andalusian region, Southern Spain. In general, machine learning models significantly outperformed the baselines. Furthermore, the accuracy dramatically dropped when forecasting ET0 for any horizon longer than three days. SVM, ELM, and RF using configurations I, III, IV, and IX outperformed, on average, the rest of the configurations in most cases. The best NSE values ranged from 0.934 in Córdoba to 0.869 in Tabernas, using SVM. The best RMSE, on average, ranged from 0.704 mm/day for Málaga to 0.883 mm/day for Conil using RF. In terms of MBE, most models and cases performed very accurately, with a total average performance of 0.011 mm/day. We found a relationship in performance regarding the aridity index and the distance to the sea. The higher the aridity index at inland locations, the better results were obtained in forecasts. On the other hand, for coastal sites, the higher the aridity index, the higher the error. Due to the good performance and the availability as an open-source repository of these models, they can be used to accurately forecast ET0 in different geo-climatic conditions, helping to increase efficiency in tasks of great agronomic importance, especially in areas with low rainfall or where water resources are limiting for the development of crops

Application of inclusive multiple model for the prediction of saffron water footprint

Applying new approaches in the management of water resources is a vital issue, especially in arid and semi-arid regions. The water footprint is a key index in water management. Therefore, it is necessary to predict its changes for future durations. The soft computing model is one of the most widely used models in predicting and estimating agroclimatic variables. The purpose of this study is to predict the green and blue water footprints of saffron product using the soft computing model. In order to select the most effective variables in prediction water footprints, the individual input was eliminated one by one and the effect of each on the residual mean square error (RMSE) was measured. In the first stage, the Group Method of Data Handling (GMDH) and evolutionary algorithms have been applied. In the next stage, the output of individual models was incorporated into the Inclusive Multiple Model (IMM) as the input variables in order to predict the blue and green water footprints of saffron product in three homogenous agroclimatic regions. Finally, the uncertainty of the model caused by the input and parameters was evaluated. The contributions of this research are introducing optimized GMDH and new ensemble models for predicting BWF, and GWF, uncertainty analysis and investigating effective inputs on the GWF and BWF. The results indicated that the most important variables affecting green and blue water footprints are plant transpiration, evapotranspiration, and yield, since removing these variables significantly increased the RMSE (range=11–25). Among the GMDH models, the best performance belonged to NMRA (Naked Mole Ranked Algorithm) due to the fast convergence and high accuracy of the outputs. In this regard, the IMM has a better performance (FSD=0.76, NSE=0.95, MAE) = 8, PBIAS= 8) than the alternatives due to applying the outputs of several individual models and the lowest uncertainty based on the parameters and inputs of the model (p = 0.98, r = 0.08)

Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm

Evapotranspiration, as a combination of evaporation and transpiration of water vapour, is a primary component of global hydrological cycles. It accounts for significant loss of soil moisture from the earth to the atmosphere. Reliable methods to monitor and forecast evapotranspiration are required for decision-making. Reference evapotranspiration, denoted as ET , is a major parameter that is useful in quantifying soil moisture in a cropping system. This article aims to design a multi-stage deep learning hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Multivariate Empirical Mode Decomposition (MEMD) and Boruta-Random Forest (Boruta) algorithms to forecast ET in the drought-prone regions ( i.e ., Gatton, Fordsdale, Cairns) of Queensland, Australia. Daily data extracted from NASA’s Goddard Online Interactive Visualization and Analysis Infrastructure (GIOVANNI) and Scientific Information for Land Owners (SILO) repositories over 2003–2011 are used to build the proposed multi-stage deep learning hybrid model, i.e ., MEMD-Boruta-LSTM, and the model’s performance is compared against competitive benchmark models such as hybrid MEMD-Boruta-DNN, MEMD-Boruta-DT, and a standalone LSTM, DNN and DT model. The test MEMD-Boruta-LSTM hybrid model attained the lowest Relative Root Mean Square Error (≤17%), Absolute Percentage Bias (≤12.5%)and the highest Kling-Gupta Efficiency (≥0.89%) relative to benchmark models for all study sites. The proposed multi-stage deep hybrid MEMD-Boruta-LSTM model also outperformed all other benchmark models in terms of predictive efficacy, demonstrating its usefulness in the forecasting of the daily ET dataset. This MEMD-Boruta-LSTM hybrid model could therefore be employed in practical environments such as irrigation management systems to estimate evapotranspiration or to forecast ET

Development of Deep Learning Hybrid Models for Hydrological Predictions

The Abstract is currently unavailable, due to the thesis being under Embargo

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Reference Evapotranspiration Estimation Using ANN, LSSVM, and M5 Tree Models (Case Study: of Babolsar and Ramsar Regions, Iran)

Evapotranspiration is a non-linear and complex phenomenon requiring different climatic variables for accurate estimation. In this study, the performance of several artificial intelligence models in estimating the amount of monthly reference evapotranspiration was investigated. Babolsar and Ramsa regions located in the north of Iran were selected as case study models proposed in this study: artificial neural network (ANN), least square support vector machines (LSSVM), and M5 tree models. The data used in this study was gathered between 2009 till 2019 (11 consecutive years). In the present study, 70% of the data were used for the training stage, and 30% of the data were reserved for testing the proposed models. Models' performances were evaluated using several evaluation criteria, i.e., the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute error (MAE). The results for Babolsar and Ramsar stations showed that all three models have a relatively good performance in estimating the rate of reference evapotranspiration. However, the LSSVM model performed better than the other models. The R2, MAE, and RMSE for the LSSVM model in the test stage were 0.982, 0.366 mm, 0.425 mm, 0.937, 0.018 mm, and 0.350 mm for Babolsar and Ramsar stations, respectively