20 research outputs found

    Smart models to improve agrometeorological estimations and predictions

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    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

    Combined forecast model involving wavelet-group methods of data handling for drought forecasting

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    Vigorous efforts to improve the effectiveness of drought forecasting models has yet to yield accurate result. The situation gives room on the use of robust forecasting methods that could effectively improve existing methods. The complex nature of time series data does not enable one single method that is suitable in all situations. Thus, a combined model that will provide a better result is then proposed. This study introduces a wavelet and group methods of data handling (GMDH) by integrating discrete wavelet transform (DWT) and GMDH with transfer functions such as sigmoid and radial basis function (RBF) to form three wavelet-GMDH models known as modified W-GMDH (MW-GMDH), sigmoid W-GMDH (SW-GMDH) and RBF W-GMDH. To assess the effectiveness of this approach, these models were applied to rainfall data at four study stations namely Arau and Kuala Krai in Malaysia as well as Badeggi and Duku-Lade in Nigeria. These data were transformed into four Standardized Precipitation Index (SPI) known as SPI3, SPI6, SPI9 and SPI12. The result shows that the integration of DWT improved the performance of the conventional GMDH model. The combination of these models further improved the performance of each model. The proposed model provides efficient, simple, and reliable accuracy when compared with other models. The incorporation of wavelet to the study results in improving performance for all four stations with the Combined W-GMDH (CW-GMDH) and Combined Regression W-GMDH (CRW-GMDH) models. The results show that Duku-Lade station produced the lowest value of 0.0239 and 0.0211 for RMSE and MAE and highest value of 0.9858 for R respectively. In addition, CRW-GMDH model produce the lowest value of 0.0168 and 0.0117, and the highest value of 0.9870 for RMSE MAE, and R respectively. On the percentage improvement, Duku-Lade station shows improvement over other models with the reductions in RMSE and MAE by 42.3% and 80.3% respectively. This indicates that the model is most suitable for the drought forecasting in this station. The results of the comparison among the four stations indicate that the CW-GMDH and CRW-GMDH models are more accurate and perform better than MW-GMDH, SW-GMDH and RBFW-GMDH models. However, the overall performance of the CRW-GMDH model outweigh that of the CW-GMDH model. In conclusion, CRW-GMDH model performs better than other models for drought forecasting and capable of providing a promising alternative to drought forecasting technique

    Water Resources Management and Modeling

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    Hydrology is the science that deals with the processes governing the depletion and replenishment of water resources of the earth's land areas. The purpose of this book is to put together recent developments on hydrology and water resources engineering. First section covers surface water modeling and second section deals with groundwater modeling. The aim of this book is to focus attention on the management of surface water and groundwater resources. Meeting the challenges and the impact of climate change on water resources is also discussed in the book. Most chapters give insights into the interpretation of field information, development of models, the use of computational models based on analytical and numerical techniques, assessment of model performance and the use of these models for predictive purposes. It is written for the practicing professionals and students, mathematical modelers, hydrogeologists and water resources specialists

    Data-Driven Techniques for Monthly Pan Evaporation Modeling in Iraq

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    Evaporation plays significant roles in agricultural production, climate change and water resources management. Hence, its accurate prediction is of paramount importance. This study aimed at investigating the potentials of artificial neural network (ANN), support vector regression (SVR) and classical multiple linear regression (MLR) models for monthly pan evaporation modeling in Erbil and Salahaddin stations of Iraq. Data including maximum, minimum, and mean temperatures, wind speed, relative humidity, and vapor pressure were used as inputs for 5 different input combinations to achieve the study objective. For performance evaluation of the applied models, root mean square error (RMSE) and determination coefficient (DC) were employed. In addition, Taylor diagrams were plotted to compare the performance of the models. The results showed that models with 6 inputs provided the best performance for Salahaddin station, but 5 inputs model led to better accuracy for MLR model in Erbil station. ANN provided superior performance with DC = 0.9527 and RMSE = 0.0660 for Erbil station while for Salahaddin station, SVR performed better with DC and RMSE of 0.8487 and 0.0753 in the validation phase. The general study results demonstrated that all the 3 applied models could be employed for successful pan evaporation modeling in the study stations, but for better accuracy, ANN is preferable

    Simulación y pronóstico de caudales diarios del Río Amazonas usando un enfoque híbrido Wavelet y Redes Neuronales

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    Universidad Nacional Agraria La Molina. Escuela de Posgrado. Maestría en Recursos HídricosEl incremento de eventos extremos durante las últimas décadas en la cuenca amazónica, ha dado lugar a un creciente interés por implementar efectivos sistemas de pronóstico hidrológico. Los pronósticos a corto plazo, como parte intrínseca de estos sistemas, son fundamentales en la mitigación de inundaciones, y la gestión de los recursos hídricos. Debido a la importancia de los pronósticos de alta calidad y a la complejidad de los sistemas hidrológicos, se han estudiado un gran número de métodos de modelamiento orientado a pronósticos. En esta investigación, se desarrollaron modelos “basados en datos” con dos técnicas, la red neuronal artificial (RNA) y un enfoque híbrido que combina análisis multiresolución wavelet y RNA llamado modelo wavelet red neuronal (WRN). En efecto, se formularon distintas estructuras de modelos univariados de RNA y WRN para múltiples horizontes de pronóstico, considerando que la confiabilidad de pronóstico disminuye al aumentar el tiempo de anticipación. Para el cual, se empleó series observadas de caudales diarios para el periodo 1985-2012, registrados en la estación hidrológica de Tamshiyacu en el río Amazonas, Perú. Además, el desempeño de los modelos se evaluó en función a los índices estadísticos, tales como la raíz del error cuadrático medio (RMSE) y la eficiencia de Nash-Sutcliffe (NSE). Así, para el horizonte de pronóstico más lejano (30 días), se encontró que el modelo WRN con RMSE = 4820 m3 /s y NSE = 0.83 superó ampliamente al modelo RNA con RMSE = 6092 m3 /s y NSE = 0.72, en la etapa de validación. Estos hallazgos muestran que el modelo híbrido tiene la capacidad potencial para mejorar la precisión de pronóstico en comparación al modelo RNA convencional. En suma, los resultados de esta investigación ayudarán a los hidrólogos y tomadores de decisiones en el pronóstico de caudales y la gestión sostenible de los recursos hídricos.The increasing number of extreme events during the last decades in the Amazon basin has led to a growing interest in implementing effective hydrological forecasting systems. Short-term forecasts, as an intrinsic part of these systems, are crucial for flood mitigation and water resources management. Due to the importance of high-quality forecasting and the complexity of hydrological systems, a large number of forecasting-oriented modelling methods have been studied. In this research, data-driven models with two techniques were developed, artificial neural network (ANN) and a hybrid approach which combines wavelet multi-resolution analysis and ANN named wavelet neural network (WNN) model. In effect, several structures of univariate ANN and WNN models were formulated for multiple forecasting horizons, considering that the reliability of forecasting decreases with increasing the lead-time. For which, observed time series of daily streamflows for the period 1985-2012 recorded at the Tamshiyacu gauging station on the Amazon river, Peru, were used. In addition, the performance of the models has been evaluated based on the statistical indices, such as root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE). Thus, for longer lead-time forecasting (30 days), it was found that the WNN model with RMSE = 4820 m3 /s and NSE = 0.83, widely outperformed to ANN model with RMSE = 6092 m3 /s and NSE = 0.72, in the test period. These findings show that the hybrid WNN model has the potential ability to improve the forecasting accuracy compared to the conventional ANN model. In sum, the outcomes of this research will assist hydrologists and decision makers in streamflow forecasting and sustainable management of water resources

    Three layer wavelet based modeling for river flow

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    All existing methods regarding time series forecasting have always been challenged by the continuous climatic change taking place in the world. These climatic changes influence many unpredictable indefinite factors. This alarming situation requires a robust forecasting method that could efficiently work with incomplete and multivariate data. Most of the existing methods tend to trap into local minimum or encounter over fitting problems that mostly lead to an inappropriate outcome. The complexity of data regarding time series forecasting does not allow any one single method to yield results suitable in all situations as claimed by most researchers. To deal with the problem, a technique that uses hybrid models has also been devised and tested. The applied hybrid methods did bring some improvement compared to the individual model performance. However, most of these available hybrid models exploit univariate data that requires huge historical data to achieve precise forecasting results. Therefore, this study introduces a new hybrid model based on three layered architecture: Least Square Support Vector Machine (LSSVM), Discrete Wavelet Transform (DWT), correlation (R) and Kernel Principle Components Analyses (KPCA). The three-staged architecture of the proposed hybrid model includes Wavelet-LSSVM and Wavelet-KPCA-LSSVM enabling the model to present itself as a well-established alternative application to predict the future of river flow. The proposed model has been applied to four different data sets of time series, taking into account different time series behavior and data scale. The performance of the proposed model is compared against the existing individual models and then a comparison is also drawn with the existing hybrid models. The results of WKPLSSVM obtained from Coefficient of Efficiency (CE) performance measuring methods confirmed that proposed model has encouraging data of 0.98%, 0.99%, 0.94% and 0.99% for Jhelum River, Chenab River, Bernam River and Tualang River, respectively. It is more robust for all datasets regardless of the sample sizes and data behavior. These results are further verified using diverse data sets in order to check the stability and adaptability. The results have demonstrated that the proposed hybrid model is a better alternative tool for time series forecasting. The proposed hybrid model proves to be one of the best available solutions considering the time series forecasting issues

    Application of Data-Driven and Process-Based Modeling Approaches for Water Quality Simulation in Lakes and Freshwater Reservoirs

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    Lakes and freshwater reservoirs often serve as the primary drinking and irrigation water sources for surrounding communities. They provide recreational and tourism opportunities, thereby promoting the prosperity of neighboring communities. Reliable estimates of water quality in lakes and reservoirs can improve management practices to protect water resources. Seasonal water temperature and solar shortwave radiation variations, and their subsequent interactions with water column aquatic life, combined with seasonal variations of mixing intensity throughout the water column, result in variations of water quality constituents with depth during the annual cycle. The complexity of these variations entails the use of advanced water quality modeling approaches to evaluate the trends of water quality variations over time. The current study presents two different modeling approaches for water quality modeling in lakes and reservoirs. In the first approach, a three-dimensional process-based model (AEM3D, HydroNumerics Pty Ltd.) was used for hydrodynamic modeling of Lake Arrowhead, California. The model was calibrated based on in-situ measured meteorological and water quality data. The calibrated process-based model was able to simulate water temperature and salinity profiles in the lake at different depths from May 2018 to April 2019, with mean relative errors of less than 6.1% and 4.2%, respectively. The model was also used to evaluate the mixing intensities at different depths during the study period. The second approach employed two separate data-driven models incorporating wavelet transform and artificial neural networks for water quality modeling of Boulder Basin, Lake Mead. The first data-driven model proposed a cost-effective method for estimating water quality profiles based on environmental data measured at the water surface. The model could estimate water temperature, dissolved oxygen, and electrical conductivity profiles from May 2011 to January 2015 with mean relative errors of 0.52%, 0.62%, and 0.22%, respectively. The second data-driven model was designed to forecast future water quality variations at different depths in Boulder Basin, Lake Mead. This model used a time step of 6 hours based on the availability of water quality data, and forecasted up to 960 step-ahead (240 days) water quality profiles in the basin. The data-driven model was able to successfully forecast 180-day ahead water temperature, dissolved oxygen, and electrical conductivity profiles in the basin with relative errors of less than 7.5%, 15.5%, and 4.7%, respectively. Results of this study can benefit water management practices to evaluate different water quality modeling approaches and select appropriate methods based on their needs and budget to simulate water quality variations of their lakes and reservoirs

    Month ahead rainfall forecasting using gene expression programming

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    In the present study, gene expression programming (GEP) technique was used to develop one-month ahead monthly rainfall forecasting models in two meteorological stations located at a semi-arid region, Iran. GEP was trained and tested using total monthly rainfall (TMR) time series measured at the stations. Time lagged series of TMR samples having weak stationary state were used as inputs for the modeling. Performance of the best evolved models were compared with those of classic genetic programming (GP) and autoregressive state-space (ASS) approaches using coefficient of efficiency (R2) and root mean squared error measures. The results showed good performance (0.53<R2<0.56) for GEP models at testing period. In both stations, the best model evolved by GEP outperforms the GP and are significantly superior to the ASS models.No sponso

    Assessment of Trend in Groundwater Level using Hybrid Mann-Kendall and Wavelet Transform Method (Case Study: Ardabil Plain)

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    Study of changes in groundwater resources has great importance on planning and management of sustainable water resources in any region.  The goal of this study was trends and dominant period investigation in groundwater level data at monthly timescales in fifteen piezometers of Ardabil plain using non-parametric Mann–Kendall (MK), temporal pre-processing (discrete wavelet transform) and spatial pre-processing (self-organizing map) methods. In first step, a Self-Organizing-Map (SOM)-based clustering technique was used to identify spatially homogeneous clusters of groundwater level (GWL) data. At second step, the wavelet transform (WT) was also used to extract dynamic and multi-scale features of the non-stationary GWL for central piezometers at 3 level. At last step, The MK test were applied to different combinations of DWT after removing the effect of significant lag-1 serial correlation to calculate components responsible for trend of the time series.  The results showed that negative trend is prevalent in the case study; generally, wavelet-based detail at level 3 plus the approximations time series was conceded as the dominant periodic component

    Using Wavelet to Analyze Periodicities in Hydrologic Variables

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    The trend and shift in the seasonal temperature, precipitation and streamflow time series across the Midwest have been analyzed, for the period 1960-2013, using the statistical analyses (Mann- Kendall test with and without considering short term persistence (MK2 and MK1, respectively) and Pettitt test). The paper also utilizes a relatively new approach, wavelet analysis, for testing the existence of trend and shift in the time series. The method has the ability to decompose a time series in to lower (trend) and higher frequency components (noise). Discrete wavelet transform (DWT) has been employed in the present study with an aim to find which periodicities are mainly responsible for trend in the original data. The combination of MK1, MK2, and DWT along with Pettitt test hasn’t been extensively used up to this time, especially in detecting trend and shift in the Midwest. The analysis of climate division temperature and precipitation data and USGS naturalized streamflow data revealed the presence of periodicity in the time series data. All the incorporated time series data were seasonal to analyze the trends and shifts for four seasons-winter, spring, summer and fall independently. D3 component of DWT were observed to be influential in detecting real trend in temperature, precipitation and streamflow data, however unlike temperature, precipitation and streamflow showed decreasing trend as well. Shift was relatively observed more than trend in the region with dominance of D3 component in the data. The result indicate the significant warming trend which agrees with the “increasing temperature” observations in the past two decades, however a clear explanation for precipitation and streamflow is not obvious
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